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Dynamic niching particle swarm optimization with an external archive-guided mechanism for multimodal multi-objective optimization

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INFORMATION SCIENCES 2024年 653卷

作者： Sun, Yu Chang, Yuqing Yang, Shengxiang Wang, Fuli Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

multimodal multi-objective optimization problems (MMOPs) contain multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However, simultaneously locating well-distributed and well-converged multiple equivalent global PSs and PF remains challenging. Therefore, this paper proposes dynamic niching particle swarm optimization (PSO) with an external archive-guided (AG) mechanism, termed DNPSO-AG, for solving MMOPs. In DNPSO-AG, a clustering-based dynamic niching technique is integrated with PSO to divide the population into multiple niches. In addition, a leader updating method controls the updating of the leaders. Furthermore, a novel external archive-guided mechanism guides the evolution of multiple niches and enhances the distribution of solutions, which comprises two strategies: the adaptive division of the external archive strategy, which adaptively divides the external archive into multiple sub-archives, and the distance-based sub-archive and niche matching strategy, which assigns sub-archives to multiple niches for maintenance. The experimental results demonstrate that the proposed DNPSO-AG outperforms seven other state-of-the-art competitors on the CEC 2019 MMOP test suite in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics, with improvements of 21.3% and 9.1% over the best-performing competitor, respectively.

关键词： Particle swarm optimization Niching Affinity propagation clustering External archive multimodal multi-objective optimization

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A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems

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APPLIED SOFT COMPUTING 2022年第0期119卷 108606-108606页

作者： Hu, Yi Wang, Jie Liang, Jing Wang, Yanli Ashraf, Usman Yue, Caitong Yu, Kunjie Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Henan Univ Technol Sch Artificial Intelligence & Big Data Zhengzhou 450001 Peoples R China

multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objective space) in recent years. However, most existing methods only use single evolutionary operator to generate offsprings and ignore the advantage of using hybrid evolutionary algorithm. Moreover, it is still a great challenge to balance the effectiveness and efficiency simultaneously in the evolutionary process of MMO. In view of this, an efficient Two-Archive model based multimodal evolutionary algorithm is proposed in this paper. Two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution are applied to expand two solution spaces with different evolutionary requirements. Moreover, niching local search scheme and reverse vector mutation strategy play roles in achieving better convergence and diversity. Finally, 22 MMO test problems are used to validate the superiority of the proposed method by comparing it with 5 state-of-the-art MMO algorithms. The proposed method is also expanded to solve 9 feature selection problems for validating the effectiveness of the proposed method on real world applications. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

关键词： multimodal multi-objective optimization Two-Archive Evolutionary algorithm Particle swarm optimizer Differential evolution

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A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2019年 50卷 100569-000页

作者： Zhang, Weizheng Li, Guoqing Zhang, Weiwei Liang, Jing Yen, Gary G.

In the multimodal multi-objective optimization problems (MMOPs), there exists more than one Pareto optimal solutions in the decision space corresponding to the same location on the Pareto front in the objective space. To solve the MMOPs, the designed algorithm is supposed to converge to the accurate and well-distributed Pareto front, and at the same time to search for the multiple Pareto optimal solutions in the decision space. This paper presents a new cluster based particle swarm optimization algorithm (PSO) with leader updating mechanism and ring-topology for solving MMOPs. multiple subpopulations are formed by a new decision variable clustering method with the aim of searching for the multiple Pareto optima solutions and maintaining the diversity. Globalbest PSO is employed for independent evolution of subpopulations, while local-best PSO with ring topology is used to enhance the information interaction among subpopulations. Seamlessly integrated, the proposed algorithm provides a good balance between exploration and exploitation. In addition, leader updating strategy is introduced to identify the best leaders in PSO. The performance of the proposed algorithm is compared with six state-of-the-art designs over 11 multimodal multi-objective optimization test functions. Experimental results demonstrate the effectiveness of the proposed algorithm.

关键词： Particle swarm optimization Clustering Ring topology multimodal multi-objective optimization

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A multi-objective particle swarm optimizer based on reference point for multimodal multi-objective optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年 107卷 104523-104523页

作者： Li, Guosen Zhou, Ting Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Yangtze Univ Sch Informat & Math Jingzhou 434000 Hubei Peoples R China

In real-world applications, there are many multimodal multi-objective optimization problems, which have multiple equivalent global Pareto-optimal solutions or with at least one local Pareto-optimal solution in the decision space. While some evolutionary algorithms have been proposed to find the global solutions recently, they are difficult to handle multimodal multi-objective optimization problems with local solutions. Meanwhile, there have been few studies on searching for local Pareto solutions. However, local solutions are additional alternatives for the decision makers if global solutions are impracticable. This paper proposes a particle swarm optimizer based on reference point, termed RPPSO, which combines a reference point mechanism and a local solution preserving technique. The reference point strategy is utilized to establish multiple evenly distributed neighborhoods and guide particles to evolve independently in their respective neighborhoods, so as to detect more Pareto solutions in the decision space. The local solution preserving technique is employed to estimate the dominant radius of each front, and with this radius to classify the individuals as either non-local or local solutions with the aim of retaining the local solutions. In addition, a set of benchmark test functions with local Pareto solutions are designed. The proposed algorithm is comprehensively evaluated on forty-four benchmark functions and is compared with fourteen state-of-the-art algorithms. The experimental results show that the proposed RPPSO achieves competitive performance than its competitors in terms of the reciprocal of Pareto sets proximity (rPSP). The RPPSO is also applied to solve on one real-world problem (i.e., map-based problem) to further verify the effectiveness and efficiency.

关键词： Particle swarm optimization multimodal multi-objective multimodal multi-objective optimization

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Growing Neural Gas Network-based surrogate-assisted Pareto set learning for multimodal multi-objective optimization

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 87卷

作者： Ming, Fei Gong, Wenyin Jin, Yaochu China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China

The key issue in handling multimodal multi -objective optimization problems (MMOPs) is to find multiple Pareto sets (PSs) corresponding to one Pareto front (PF). Therefore, learning the PSs is critical to facilitate solving MMOPs while unfortunately, current research only focuses on PF learning which is helpless in finding multiple PSs by the information of one PF. Moreover, since the PSs of an MMOP are usually non-functional, traditional approximative function model -based PF learning is inapplicable. Consequently, developing new PS learning techniques is desired. Inspired by data -driven evolutionary algorithms, data can be used to train surrogate models to assist the algorithm. This article proposes an online data -driven PSs learning technique that aims to learn the topologies of PSs through a surrogate model to facilitate the search for PSs. Specifically, the Growing Neural Gas network is trained using non -dominated solutions to learn the topologies of PSs during the evolutionary process. Then, the nodes of the network are used to generate new solutions and adopted as reference points for environmental selection. A new algorithm is developed based on the PS learning technique for MMOPs. Experimental studies on three benchmark test suites and two different real -world applications demonstrate the superiority of our method over six state-of-the-art algorithms dedicated to MMOPs. The PSs learning technique can obtain the topologies of PSs and facilitate the search for them.

关键词： multimodal multi-objective optimization Pareto set learning Data-driven Surrogate-assisted evolutionary algorithms Growing Neural Gas

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A Particle Swarm Optimizer with adaptive dynamic neighborhood for multimodal multi-objective optimization

A Particle Swarm Optimizer with adaptive dynamic neighborhoo...

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第35届中国控制与决策会议

作者： Jingyue Wei Enze Zhang Rui Ge Information Engineering College of Yangzhou University

In this paper,a multi-objective particle swarm optimizer based on adaptive dynamic neighborhood(ADNMOPSO) is proposed to locate multiple Pareto optimal solutions to solve multimodal multi-objective *** the proposed algorithm,a spatial distance-based non-overlapping ring topology is used to form multiple subpopulations for parallel search to enhance the local search capability of the *** addition,an adaptive dynamic neighborhood selection strategy is proposed to balance the exploration and exploitation capabilities of the algorithm,allowing the size of the subpopulation to change automatically when the neighborhood switch time is *** prevent the algorithm from premature convergence,a stagnation detection strategy is introduced to apply a Gaussian perturbation operation to the particles that fall into the neighborhood ***,the proposed algorithm is used to solve multimodal multi-objective test problems and compared with existing multimodal multiobjective optimization *** results show that the proposed algorithm can obtain more Pareto solutions when solving different types of multimodal multi-objective functions.

关键词： multi-objective optimization multimodal multi-objective optimization particle swarm optimization algorithm sub-swarm regrouping ring topology

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multi-objective optimization for multimodal multi-objective multi-Point Shortest Path Problem Considering Unforeseeable Road Eventualities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Xu, Zhiwei Zhang, Kai Del Ser, Javier Li, Miqing Xu, Xin He, Juanjuan Wu, Ni Wuhan Univ Sci & Technol Sch Comp Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc andRe Wuhan 430065 Hubei Peoples R China Tecnalia Basque Res & Technol Alliance BRTA Bizkaia 48160 Bilbao Spain Univ Basque Country Dept Commun Engn UPV EHU Bizkaia 48013 Bilbao Spain Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

multi-objective multi-point shortest path planning problems are commonly encountered in real-world applications. Numerous path planning algorithms have been proposed to accommodate different model assumptions. However, most existing algorithms can only identify a subset of the Pareto optimal paths and overlook equivalent Pareto optimal paths. Relying solely on a subset of Pareto optimal solutions is insufficient to effectively respond to unforeseeable road eventualities in the real-world traffic environment. In this paper, multi-objective multi-point shortest path planning problem is modeled as a multimodal multi-objective optimization problem with necessary points constrains. A multimodal multi-objective evolutionary algorithm using constraint dominance principle-based path comparison strategy and path similarity-based multimodal solutions selection strategy is proposed to address this problem. The proposed constraint dominance principle-based path comparison strategy can effectively navigate through large infeasible regions by relaxing necessary point constraints, thereby obtaining a true constrained Pareto front. The proposed path similarity-based multimodal solutions selection strategy can effectively balance the distribution of solutions in the decision space, thereby preserving multiple equivalent optimal solutions. The proposed algorithm is compared with five state-of-the-art path planning algorithms from the benchmark test suite derived from the 2021 IEEE CEC path planning competition, where city maps are adapted from real transportation networks in Chinese cities, in our experiments. The exceptional performance is demonstrated through thirty independent runs, yielding experimental results that showcase the superiority of the proposed algorithm on the test problem set. This superior performance highlights the potential for designing more resilient path planners suitable for scenarios affected by unpredictable road eventualities.

关键词： Path planning optimization Classification algorithms Shortest path problem Pareto optimization Search problems Heuristic algorithms Roads Evolutionary computation Convergence multi-objective shortest path planning constrained multi-objective optimization multimodal multi-objective optimization multi-objective evolutionary algorithm

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Handling multimodal multi-objective problems through self-organizing quantum-inspired particle swarm optimization

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INFORMATION SCIENCES 2021年 577卷 510-540页

作者： Li, Guoqing Wang, Wanliang Zhang, Weiwei You, Wenbo Wu, Fei Tu, Hangyao Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Zhengzhou Univ Light Ind Sch Comp & Commun Engn Zhengzhou 450000 Peoples R China

multimodal multi-objective optimization problems (MMOPs) involve locating equivalent Pareto optimal solutions in decision space with the same objective values. The key to handling MMOPs is finding all equivalent Pareto optimal solutions and maintaining a promising balance between the convergence and diversity of solutions in both decision space and objective space. To tackle this issue, a self-organizing quantum-inspired particle swarm optimization algorithm (MMO_SO_QPSO) is proposed in this paper for handling MMOPs. In the proposed MMO_SO_QPSO, a self-organizing map is used to find the best neighbor leader of particles. With the aid of neighbor leader particles, a special zone searching method is adopted to update the position of particles and locate equivalent Pareto optimal solutions in decision space. To maintain diversity and convergence of Pareto optimal solutions, a special archive mechanism that relies on the maximum-minimum distance among solutions is introduced into MMO_SO_QPSO. And some outstanding Pareto optimal solutions are maintained in the special archive. In addition, a new performance indicator is developed to estimate properly the similarity between obtained Pareto optimal solutions and true Pareto optimal solutions. The performance of the proposed MMO_SO_QPSO is compared with six state-of-the-art multimodal multi-objective evolutionary algorithms on two well-known benchmark problems. Experimental results demonstrate the superior performance of MMO_SO_QPSO for solving MMOPs. The effectiveness of several strategies is also discussed. (c) 2021 Elsevier Inc. All rights reserved.

关键词： multimodal multi-objective optimization problems Self-organizing Quantum-inspired particle swarm optimization Zone searching

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Layout optimization of Indoor Obstacle Using a multimodal multi-objective Evolutionary Algorithm 13th

Layout Optimization of Indoor Obstacle Using a Multimodal Mu...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Wu, Tianrui Liu, Qingqing Wang, Weili Fan, Qinqin Shanghai Maritime Univ Logist Res Ctr Shanghai 201306 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

To improve evacuation efficiency and safety, indoor obstacles are usually used in different scenes. However, layout of indoor obstacles may have many actual constraints;thus more equivalent or alternative schemes should be provided to decision-makers. To carry out the above objective, a multimodal multi-objective layout optimization problem of indoor obstacles is proposed in the present study. Moreover, a state-of-the-art multimodal multi-objective evolutionary algorithm is used to solve this problem. Simulation results show that the current study can provide more schemes to decision-makers for different scenario constraints when the evacuation efficiency and safety are the same.

关键词： multimodal multi-objective optimization Evolutionary computation Crowd evacuation Obstacle layout Evacuation efficiency

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An Interval multi-objective Particle Swarm optimization Algorithm with Niching Technology for multimodal Problems 35

An Interval Multi-objective Particle Swarm Optimization Algo...

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35th Chinese Control and Decision Conference (CCDC)

作者： Guan, Shouping Li, Xinyu Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9798350334722

This paper proposes an interval multi-objective niching particle swarm optimization (IMNPSO) algorithm for solving interval multimodal multi-objective optimization problems (IMMOPs). Firstly, in order to locate and maintain multiple optimal solutions, an interval niching PSO (INPSO) algorithm using a ring topology is introduced, and a dual archive mechanism is used to store personal and neighborhood best positions of all particles. Then, a fast non-dominated sorting algorithm based on interval analysis and the interval special crowding distance (ISCD) calculation method are presented, which are combined to form the sorting mechanism of interval particles in IMNPSO. Finally, several multi-objective test functions are selected, and the results demonstrate the effectiveness of the proposed algorithm.

关键词： interval particle swarm optimization (IPSO) multimodal multi-objective optimization niching technology crowding distance

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