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Vol. 6,  No. 3, 2019

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THE ALFRED NORTH WHITEHEAD LAUREATE LECTURE
A Reflection of Future in History: Introduction to The Alfred North Whitehead Laureate Lecture
Fei-Yue Wang
2019, 6(3): 609-609.
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The Need for Fuzzy AI
Jonathan M. Garibaldi
2019, 6(3): 610-622. doi: 10.1109/JAS.2019.1911465
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Artificial intelligence (AI) is once again a topic of huge interest for computer scientists around the world. Whilst advances in the capability of machines are being made all around the world at an incredible rate, there is also increasing focus on the need for computerised systems to be able to explain their decisions, at least to some degree. It is also clear that data and knowledge in the real world are characterised by uncertainty. Fuzzy systems can provide decision support, which both handle uncertainty and have explicit representations of uncertain knowledge and inference processes. However, it is not yet clear how any decision support systems, including those featuring fuzzy methods, should be evaluated as to whether their use is permitted. This paper presents a conceptual framework of indistinguishability as the key component of the evaluation of computerised decision support systems. Case studies are presented in which it has been clearly demonstrated that human expert performance is less than perfect, together with techniques that may enable fuzzy systems to emulate human-level performance including variability. In conclusion, this paper argues for the need for " fuzzy AI” in two senses: (i) the need for fuzzy methodologies (in the technical sense of Zadeh’s fuzzy sets and systems) as knowledge-based systems to represent and reason with uncertainty; and (ii) the need for fuzziness (in the non-technical sense) with an acceptance of imperfect performance in evaluating AI systems.
REVIEW
A Survey of Model Predictive Control Methods for Traffic Signal Control
Bao-Lin Ye, Weimin Wu, Keyu Ruan, Lingxi Li, Tehuan Chen, Huimin Gao, Yaobin Chen
2019, 6(3): 623-640. doi: 10.1109/JAS.2019.1911471
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Enhancing traffic efficiency and alleviating (even circumventing) traffic congestion with advanced traffic signal control (TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control (MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations, and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.
Papers
Decentralized Control for Residential Energy Management of a Smart Users' Microgrid with Renewable Energy Exchange
Raffaele Carli, Mariagrazia Dotoli
2019, 6(3): 641-656. doi: 10.1109/JAS.2019.1911462
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This paper presents a decentralized control strategy for the scheduling of electrical energy activities of a microgrid composed of smart homes connected to a distributor and exchanging renewable energy produced by individually owned distributed energy resources. The scheduling problem is stated and solved with the aim of reducing the overall energy supply from the grid, by allowing users to exchange the surplus renewable energy and by optimally planning users' controllable loads. We assume that each smart home can both buy/sell energy from/to the grid taking into account time-varying non-linear pricing signals. Simultaneously, smart homes cooperate and may buy/sell locally harvested renewable energy from/to other smart homes. The resulting optimization problem is formulated as a non-convex non-linear programming problem with a coupling of decision variables in the constraints. The proposed solution is based on a novel heuristic iterative decentralized scheme algorithm that suitably extends the Alternating Direction Method of Multipliers to a non-convex and decentralized setting. We discuss the conditions that guarantee the convergence of the presented algorithm. Finally, the application of the proposed technique to a case study under several scenarios shows its effectiveness.
Optimal Fixed-Point Tracking Control for Discrete-Time Nonlinear Systems via ADP
Ruizhuo Song, Liao Zhu
2019, 6(3): 657-666. doi: 10.1109/JAS.2019.1911453
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Based on adaptive dynamic programming (ADP), the fixed-point tracking control problem is solved by a value iteration (Ⅵ) algorithm. First, a class of discrete-time (DT) nonlinear system with disturbance is considered. Second, the convergence of a Ⅵ algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value, and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks (NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.
$H_\infty$ Consensus Control of Discrete-Time Multi-Agent Systems Under Network Imperfections and External Disturbance
Arezou Elahi, Alireza Alfi, Hamidreza Modares
2019, 6(3): 667-675. doi: 10.1109/JAS.2019.1911474
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This paper presents a distributed control protocol for consensus control of multi-agent systems (MASs) under external disturbances and network imperfections, including communication delay and random packet dropout. To comply with the discrete nature of networked systems, in contrast to most of the existing work for MASs under network imperfections, the agents are modeled by discrete-time dynamics. The communication network is considered to be undirected, its delay is considered to be time-varying but bounded, and its packet dropout is modeled by a Bernoulli distributed white sequence. Sufficient conditions in terms of linear matrix inequalities (LMIs) for asymptotic mean-square consensus stability are derived under network imperfections without considering external disturbances. A desired disturbance attenuation level in the presence of both external disturbances and network imperfections is also provided. A simulation example is given to verify the effectiveness of the proposed approach in coping with network imperfection and disturbances.
Distributed Control of Multiple-Bus Microgrid With Paralleled Distributed Generators
Bo Fan, Jiangkai Peng, Jiajun Duan, Qinmin Yang, Wenxin Liu
2019, 6(3): 676-684. doi: 10.1109/JAS.2019.1911477
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A microgrid is hard to control due to its reduced inertia and increased uncertainties. To overcome the challenges of microgrid control, advanced controllers need to be developed. In this paper, a distributed, two-level, communication-economic control scheme is presented for multiple-bus microgrids with each bus having multiple distributed generators (DGs) connected in parallel. The control objective of the upper level is to calculate the voltage references for one-bus subsystems. The objectives of the lower control level are to make the subsystems' bus voltages track the voltage references and to enhance load current sharing accuracy among the local DGs. Firstly, a distributed consensus-based power sharing algorithm is introduced to determine the power generations of the subsystems. Secondly, a discrete-time droop equation is used to adjust subsystem frequencies for voltage reference calculations. Finally, a Lyapunov-based decentralized control algorithm is designed for bus voltage regulation and proportional load current sharing. Extensive simulation studies with microgrid models of different levels of detail are performed to demonstrate the merits of the proposed control scheme.
Robust Admissibility and Stabilization of Uncertain Singular Fractional-Order Linear Time-Invariant Systems
Saliha Marir, Mohammed Chadli
2019, 6(3): 685-692. doi: 10.1109/JAS.2019.1911480
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This paper addresses the robust admissibility problem in singular fractional-order continuous time systems. It is based on new admissibility conditions of singular fractional-order systems expressed in a set of strict linear matrix inequalities (LMIs). Then, a static output feedback controller is designed for the uncertain closed-loop system to be admissible. Numerical examples are given to illustrate the proposed methods.
Progressive LiDAR Adaptation for Road Detection
Zhe Chen, Jing Zhang, Dacheng Tao
2019, 6(3): 693-702. doi: 10.1109/JAS.2019.1911459
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Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises. However, the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection (PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-the-art road detection models and is currently top of the publicly accessible benchmark leader-board.
Standardized Evaluation of Camera-based Driver State Monitoring Systems
Renran Tian, Keyu Ruan, Lingxi Li, Jialiang Le, Jeff Greenberg, Saeed Barbat
2019, 6(3): 716-732. doi: 10.1109/JAS.2019.1911483
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Driver state sensing technologies, such as vehicular systems, start to be widely considered by automotive manufacturers. To reduce the cost and minimize the intrusiveness towards driving, the majority of these systems rely on the in-cabin camera(s) and other optical sensors. With their great capabilities in detecting and intervening of driver distraction and inattention, these technologies may become key components in future vehicle safety and control systems. However, to the best of our knowledge, currently, there is no common standard available to objectively compare the performance of these technologies. Thus, it is imperative to develop one standardized process for evaluation purposes. In this paper, we propose one systematic and standardized evaluation process after successfully addressing three difficulties: 1) defining and selecting the important influential individual and environmental factors, 2) countering the effects of individual differences and randomness in driver behaviors, and 3) building a reliable in-vehicle driver head motion tracking tool to collect ground-truth motion data. We have collected data on a large scale on a commercial driver state-sensing platform. For each subject, 30 to 40 minutes of head motion data was collected and included variables, such as lighting conditions, head/face features, and camera locations. The collected data was analyzed based on a proposed performance measure. The results show that the developed process can efficiently evaluate an individual camera-based driver state sensing product, which builds a common base for comparing the performance of different systems.
Verification of Hypertorus Communication Grids by Infinite Petri Nets and Process Algebra
Dmitry A. Zaitsev, Tatiana R. Shmeleva, Jan Friso Groote
2019, 6(3): 733-742. doi: 10.1109/JAS.2019.1911486
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A model of a hypertorus communication grid has been constructed in the form of an infinite Petri net. A grid cell represents either a packet switching device or a bioplast cell. A parametric expression is obtained to allow a finite specification of an infinite Petri net. To prove properties of an ideal communication protocol, we derive an infinite Diophantine system of equations from it, which is subsequently solved. Then we present the programs htgen and ht-mcrl2-gen, developed in the C language, which generate Petri net and process algebra models of a hypertorus with a given number of dimensions and grid size. These are the inputs for the respective modeling tools Tina and mCRL2, which provide model visualization, step simulation, state space generation and reduction, and structural analysis techniques. Benchmarks to compare the two approaches are obtained. An ad-hoc induction-like technique on invariants, obtained for a series of generated models, allows the calculation of a solution of the Diophantine system in a parametric form. It is proven that the basic solutions of the infinite system have been found and that the infinite Petri net is bounded and conservative. Some remarks regarding liveness and liveness enforcing techniques are also presented.
Advanced Policy Learning Near-Optimal Regulation
Ding Wang, Xiangnan Zhong
2019, 6(3): 743-749. doi: 10.1109/JAS.2019.1911489
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Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.
Numerical Solution of the Distributed-Order Fractional Bagley-Torvik Equation
Hossein Aminikhah, Amir Hosein Refahi Sheikhani, Tahereh Houlari, Hadi Rezazadeh
2019, 6(3): 760-765. doi: 10.1109/JAS.2017.7510646
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In this paper, two numerical methods are proposed for solving distributed-order fractional Bagley-Torvik equation. This equation is used in modeling the motion of a rigid plate immersed in a Newtonian fluid with respect to the nonnegative density function. Using the composite Boole's rule the distributed-order Bagley-Torvik equation is approximated by a multi-term time-fractional equation, which is then solved by the Grunwald-Letnikov method (GLM) and the fractional differential transform method (FDTM). Finally, we compared our results with the exact results of some cases and show the excellent agreement between the approximate result and the exact solution.
A Hybrid Offline-Online Approach to Adaptive Downlink Resource Allocation Over LTE
Satish Kumar, Rajesh Devaraj, Arnab Sarkar
2019, 6(3): 766-777. doi: 10.1109/JAS.2018.7511105
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Radio resource management mechanisms in current and future wireless networks is expected to face an enormous challenge due to the ever increasing demand for bandwidth and latency sensitive applications on mobile devices. This is because an optimal resource allocation scheme which attempts to multiplex the available bandwidth in order to maximize Quality of service (QoS), will pose an exponential computational burden at eNodeB. In order to minimize such computational overhead, this work proposes a hybrid offline-online resource allocation strategy which effectively allocates all the available resources among flows such that their QoS requirements are satisfied. The flows are firstly classified into priority buckets based on real-time criticality factors. During the offline phase, the scheduler attempts to maintain the system load within a pre-specified safe threshold value by selecting an appropriate number of buckets. This offline selection procedure makes use of supervisory control theory of discrete event systems to synthesize an offline scheduler. Next, we have devised an online resource allocation strategy which runs on top of the offline policy and attempts to minimize the impact of the inherent variability in wireless networks. Simulation results show that the proposed scheduling framework is able to provide satisfactory QoS to all end users in most practical scenarios.
Infant Cry Language Analysis and Recognition: An Experimental Approach
Lichuan Liu, Wei Li, Xianwen Wu, Benjamin X. Zhou
2019, 6(3): 778-788. doi: 10.1109/JAS.2019.1911435
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Recently, lots of research has been directed towards natural language processing. However, the baby's cry, which serves as the primary means of communication for infants, has not yet been extensively explored, because it is not a language that can be easily understood. Since cry signals carry information about a babies' wellbeing and can be understood by experienced parents and experts to an extent, recognition and analysis of an infant's cry is not only possible, but also has profound medical and societal applications. In this paper, we obtain and analyze audio features of infant cry signals in time and frequency domains. Based on the related features, we can classify given cry signals to specific cry meanings for cry language recognition. Features extracted from audio feature space include linear predictive coding (LPC), linear predictive cepstral coefficients (LPCC), Bark frequency cepstral coefficients (BFCC), and Mel frequency cepstral coefficients (MFCC). Compressed sensing technique was used for classification and practical data were used to design and verify the proposed approaches. Experiments show that the proposed infant cry recognition approaches offer accurate and promising results.
Discrete Event System Framework for Fault Diagnosis with Measurement Inconsistency: Case Study of Rogue DHCP Attack
Mayank Agarwal, Santosh Biswas, Sukumar Nandi
2019, 6(3): 789-806. doi: 10.1109/JAS.2017.7510379
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Fault detection and diagnosis (FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy (AR), Principal component analysis (PCA), Discrete event system (DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters. Due to various reasons like faults, communication errors etc., sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc., have mechanisms to handle measurement inconsistency, however, they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent (MI) parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system (MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol (DHCP) server attack by mapping the attack to a fault in the DES framework.
Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints
Yuncheng Ouyang, Lu Dong, Lei Xue, Changyin Sun
2019, 6(3): 807-815. doi: 10.1109/JAS.2019.1911495
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In this paper, a study of control for an uncertain 2-degree of freedom (DOF) helicopter system is given. The 2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function (IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.
Fuzzy Adaptive Control of a Fractional Order Chaotic System With Unknown Control Gain Sign Using a Fractional Order Nussbaum Gain
Khatir Khettab, Samir Ladaci, Yassine Bensafia
2019, 6(3): 816-823. doi: 10.1109/JAS.2016.7510169
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In this paper we propose an improved fuzzy adaptive control strategy, for a class of nonlinear chaotic fractional order (SISO) systems with unknown control gain sign. The online control algorithm uses fuzzy logic sets for the identification of the fractional order chaotic system, whereas the lack of a priori knowledge on the control directions is solved by introducing a fractional order Nussbaum gain. Based on Lyapunov stability theorem, stability analysis is performed for the proposed control method for an acceptable synchronization error level. In this work, the Grünwald-Letnikov method is used for numerical approximation of the fractional order systems. A simulation example is given to illustrate the effectiveness of the proposed control scheme.
Robust Control for Uncertain Nonlinear Feedback Systems Using Operator-based Right Coprime Factorization
Ni Bu, Wei Chen, Longguo Jin, Yandong Zhao
2019, 6(3): 824-829. doi: 10.1109/JAS.2017.7510895
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The robust control issue for uncertain nonlinear system is discussed by using the method of right coprime factorization. As it is difficult to obtain the inverse of the right factor due to the high nonlinearity, the proving of the Bezout identity becomes troublesome. Therefore, two sufficient conditions are derived to manage this problem with the nonlinear feedback system as well as that with the uncertain nonlinear feedback system under the definition of Lipschitz norm. A simulation of temperature control is given to demonstrate the validity of the proposed method.
State and Mode Feedback Control for Discrete-time Markovian Jump Linear Systems With Controllable MTPM
Jin Zhu, Qin Ding, Maksym Spiryagin, Wanqing Xie
2019, 6(3): 830-837. doi: 10.1109/JAS.2016.7510217
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In this note, the state and mode feedback control problems for a class of discrete-time Markovian jump linear systems (MJLSs) with controllable mode transition probability matrix (MTPM) are investigated. In most achievements, controller design of MJLSs pays more attention to state/output feedback control for stability, while the system cost in practice is out of consideration. In this paper, we propose a control mechanism consisting of two parts: finite-path-dependent state feedback controller design with which uniform stability of MJLSs can be ensured, and mode feedback control which aims to decrease system cost. Differing from the traditional state/output feedback controller design, the main novelty is that the proposed control mechanism not only guarantees system stability, but also decreases system cost effectively by adjusting the occurrence probability of system modes. The effectiveness of the proposed mechanism is illustrated via numerical examples.
Position Control of a Series Elastic Actuator Based on Global Sliding Mode Controller Design
Wei Yin, Lei Sun, Meng Wang, Jingtai Liu
2019, 6(3): 850-585. doi: 10.1109/JAS.2019.1911498
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A series elastic actuator (SEA) is a powerful device in the area of human-machine integration, but it still suffers from difficult position control issues. Therefore, in this paper, an efficient approach is proposed to solve this problem. The approach design is divided into two steps: feedback linearization (FL) and global sliding mode (GSM) controller design. The bounded analysis is presented and global asymptotic convergence is analytically proven. Simulation and experiment results illustrate the effectiveness of the proposed scheme.
The Controllability, Observability, and Stability Analysis of a Class of Composite Systems with Fractional Degree Generalized Frequency Variables
Cuihong Wang, Yafei Zhao, YangQuan Chen
2019, 6(3): 859-864. doi: 10.1109/JAS.2019.1911501
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This paper is concerned with fundamental properties of a class of composite systems with fractional degree generalized frequency variables, including controllability, observability and stability. Firstly, some necessary and sufficient conditions are given to guarantee controllability and observability of such composite systems. Then we prove that the stability problem of such composite systems can be reduced to judging whether a fractional degree polynomial is stable. Finally, the stability analysis result is applied in the supervisory control of fractional-order multi-agent systems, and an example is provided to illustrate the effectiveness of the proposed methods.
PAPERS
An Embedded Feature Selection Method for Imbalanced Data Classification
Haoyue Liu, MengChu Zhou, Qing Liu
2019, 6(3): 703-715. doi: 10.1109/JAS.2019.1911447
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Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue. Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index (WGI) is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve (ROC AUC) and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of F-measure achieves excellent performance only if 20% or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.
Robust Fault Detection and Isolation Based on Finite-frequency H−/H∞ Unknown Input Observers and Zonotopic Threshold Analysis
Meng Zhou, Zhengcai Cao, Ye Wang
2019, 6(3): 750-759. doi: 10.1109/JAS.2019.1911492
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This work proposes a robust fault detection and isolation scheme for discrete-time systems subject to actuator faults, in which a bank of H−/H∞ fault detection unknown input observers (UIOs) and a zonotopic threshold analysis strategy are considered. In observer design, finite-frequency H− index based on the generalized Kalman-Yakubovich-Popov lemma and H∞ technique are utilized to evaluate worst-case fault sensitivity and disturbance attenuation performance, respectively. The proposed H−/H∞ fault detection observers are designed to be insensitive to the corresponding actuator fault only, but sensitive to others. Then, to overcome the weakness of predefining threshold for FDI decision-making, this work proposes a zonotopic threshold analysis method to evaluate the generated residuals. The FDI decision-making relies on the evaluation with a dynamical zonotopic threshold. Finally, numerical simulations are provided to show the feasibility of the proposed scheme.
Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization
Zhiming Lv, Linqing Wang, Zhongyang Han, Jun Zhao, Wei Wang
2019, 6(3): 838-849. doi: 10.1109/JAS.2019.1911450
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For multi-objective optimization problems, particle swarm optimization (PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space (the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, $ \varepsilon $-Pareto active learning ($ \varepsilon $-PAL) method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter $ \varepsilon $. Therefore, a greedy search method is presented to determine the value of $ \varepsilon $ where the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines (MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization (MOPSO) algorithms.