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Parallel multi-swarm PSO strategies for solving many objective optimization problems

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2019年 126卷 13-33页

作者： de Campos, Arion, Jr. Pozo, Aurora T. R. Duarte, Elias P., Jr. State Univ Ponta Grossa UEPG Dept Informat Ponta Grossa Brazil Fed Univ Parana UFPR Dept Informat Curitiba Parana Brazil

In this work we present two parallel PSO strategies based on multiple swarms to solve MaOPs (many objective optimization problems). The first strategy is based on Pareto dominance and the other is based on decomposition. Multiple swarms execute on independent processors and communicate using broadcast on a fully connected network. We investigate the impact of using both synchronous and asynchronous communication strategies for the decomposition-based approach. Experimental results were obtained for several benchmark problems. It is possible to conclude that the parallelization has a positive effect on the convergence and diversity of the optimization process for problems with many objectives. However, there is no single strategy that is the best results for all classes of problems. In terms of scalability, for higher numbers of objectives the parallel algorithms based on decomposition are always either the best or present comparable results with the Pareto approach. There are exceptions, but only when the problem itself has discontinuities on the Pareto Front. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Evolutionary computing Multi-swarm PSO many-objective optimization problems

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Diversity Improvement in Decomposition-Based Multi-objective Evolutionary Algorithm for many-objective optimization problems

Diversity Improvement in Decomposition-Based Multi-Objective...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Zhenan He Gary G. Yen School of Electrical and Computer Engineering Oklahoma State University

ISBN: (纸本)9781479938414

Decomposition-Based Multi-objective Evolutionary Algorithms (DBMOEA), such as Multiple Single objective Pareto Sampling (MSOPS) and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), have been successfully applied in finding Pareto-optimal fronts in Multiobjective optimization problems (MOPs), two or three-objective in general. DBMOEA decomposes one MOP into multiple Single-objective optimization problems (SOPs) where the convergence of approximated front is facilitated by finding the optimal solution of each SOP and its diversity is preserved by a group of well distributed SOPs. However, when solving problems with many objectives, one single solution can be the optimal solution of multiple SOPs which inadvertently leads to a severe loss of population diversity. In this paper, we propose a new diversity improvement method incorporated into a modified DBMOEA to directly handle this challenge. The design includes two steps. First, a few number of weight vectors guide the whole population towards a small number of solutions nearby the true Pareto front. Afterwards, initialize a subpopulation around each solution and diversify them toward well distribution. As a case study, a new algorithm based on this design is compared with three state-of-the-art DBMOEAs, MOEA/D, MSOPS, and MO-NSGA-II. Experimental results show that the proposed methods exhibit better performance in both convergence and diversity than the chosen competitors for solving many-objective optimization problems.

关键词： Pareto optimisation evolutionary computation sampling methods DBMOEA MO-NSGA-II algorithm MOEA/D algorithm MSOPS algorithm Pareto-optimal front decomposition-based multiobjective evolutionary algorithm diversity improvement method many-objective optimization problems multiobjective evolutionary algorithm based on decomposition multiple single objective Pareto sampling algorithm single-objective optimization problem Diversity methods Evolutionary computation Measurement optimization Sociology Statistics Vectors

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AIEA: An Asynchronous Influence-Based Evolutionary Algorithm for Expensive many-objective optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第2期55卷 786-799页

作者： Wei, Feng-Feng Chen, Wei-Neng Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Buildingand Urban Sci Guangzhou 510006 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 30071 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea

In expensive multi/many-objective optimization problems (EMOPs), the expensive objectives are generally accessed through different simulation tools, leading to different evaluation latencies and unbearable computational time for serial optimization. One promising approach to improve efficiency is to perform simulation and build surrogates separately for each objective in parallel. However, how to improve the model accuracy and select promising candidates without global information are big challenges. To alleviate these problems, this article proposes an asynchronous influence-based SAEA (AIEA) based on the client-server model. Each client approximates an objective and the server takes charge for evolution. To adaptively select promising candidates, the influence degree is introduced in candidate selection, which is calculated in the objective space to judge which candidate has more beneficial influence for evolution. With the selected candidate, the most-uncertain-first strategy is devised in objective selection for asynchronous evaluations and model improvement. To handle incomplete objective values, the nearest neighbor inheritance is adopted for unevaluated objectives. Comprehensive experiments compared with five surrogate-assisted EAs demonstrate the global optimization and scalability of AIEA.

关键词： optimization Computational modeling Predictive models Training data Data models Vectors Servers Program processors Evolutionary computation Distributed databases Expensive optimization many-objective optimization problems parallel and distributed evolutionary computation surrogate-assisted evolutionary algorithm

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Micro many-objective Evolutionary Algorithm With Knowledge Transfer

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 43-56页

作者： Peng, Hu Luo, Zhongtian Fang, Tian Zhang, Qingfu Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiujiang Key Lab Digital Technol Jiujiang 332005 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China

Computational effectiveness and limited resources in evolutionary algorithms are interdependently handled during the working of low-power microprocessors for real-world problems, particularly in many-objective evolutionary algorithms (MaOEAs). In this respect, the balance between them will be broken by evolutionary algorithms with a normal-sized population, but which doesn't include a micro population. To tackle this issue, this paper proposes a micro many-objective evolutionary algorithm with knowledge transfer (mu MaOEA). To address the oversight that knowledge is often not considered enough between niches, the knowledge-transfer strategy is proposed to bolster each unoptimized niche through optimizing adjacent niches, which enables niches to generate better individuals. Meanwhile, a two-stage mechanism based on fuzzy logic is designed to settle the conflict between convergence and diversity in many-objective optimization problems. Through efficient fuzzy logic decision-making, the mechanism maintains different properties of the population at different stages. Different MaOEAs and micro multi-objective evolutionary algorithms were compared on benchmark test problems DTLZ, MaF, and WFG, and the results showed that mu MaOEA has an excellent performance. In addition, it also conducted simulation on two real-world problems, MPDMP and MLDMP, based on a low-power microprocessor. The results indicated the applicability of mu MaOEA for low-power microprocessor optimization.

关键词： optimization Evolutionary computation Knowledge transfer Vectors Convergence Microprocessors Fuzzy logic Micro many-objective evolutionary algorithm many-objective optimization problems knowledge transfer low-power microprocessors

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Convergence-Driven Adaptive many-objective Particle Swarm optimization

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IEEE ACCESS 2025年 13卷 5129-5144页

作者： Yi, Yunfei Wang, Zhiyong Shi, Yunying Song, Zhengzhuo Zhao, Binbin Hechi Univ Sch Big Data & Comp Hechi 546300 Peoples R China Guangxi Normal Univ Coll Comp Sci & Engn GuiLin 541004 Peoples R China Guangxi Univ Sci & Technol Sch Automat Liuzhou 545616 Peoples R China

In recent years, the prevalence of many-objective optimization problems (MaOPs) in practical applications has been increasing. However, traditional multi-objective optimization algorithms, such as Multiple objective Particle Swarm optimization (MOPSO), often face challenges of dimensionality and selection pressure when handling MaOPs. To overcome these challenges, this study proposes a Convergence-Driven Adaptive many-objective Particle Swarm optimization (CDA-MOPSO) algorithm. This algorithm introduces a convergence metric to assess the convergence status and solution distribution quality of the particle swarm during iterations. Based on this metric, Convergence-Aware Learning Factor Adjustment (CALFA), Convergence-Oriented Dimension Variation Strategy (CODVS), and Convergence-Driven Archive Maintenance (CDAM) operations are proposed. Additionally, evolutionary search is further conducted on the external archive to enhance algorithm performance. To validate the performance of the CDA-MOPSO algorithm, extensive experiments are conducted using standard test problems such as DTLZ and WFG. Experimental results demonstrate that the CDA-MOPSO algorithm exhibits superior convergence and solution distribution characteristics across multiple standard test functions, particularly in handling many-objective optimization problems, outperforming traditional multi-objective algorithms significantly. In conclusion, the CDA-MOPSO algorithm provides a novel solution for many-objective optimization problems, offering strong convergence capability and solution diversity, with broad prospects for practical applications.

关键词： optimization Convergence Particle swarm optimization Maintenance Heuristic algorithms Vectors Search problems Partitioning algorithms Measurement Clustering algorithms many-objective optimization problems particle swarm optimization convergence

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User-oriented many-objective cloud workflow scheduling based on an improved knee point driven evolutionary algorithm

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KNOWLEDGE-BASED SYSTEMS 2017年第Nov.1期135卷 113-124页

作者： Ye, Xin Liu, Sihao Yin, Yanli Jin, Yaochu Dalian Univ Technol Inst Informat & Decis Technol Dalian Liaoning Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

Cloud computing is able to deliver large amount of computing resources on demand, and it has become one of the most effective ways to implement large-scale computationally intensive applications. In a cloud computing environment, applications typically involve workflows. Therefore, optimized workflow scheduling can greatly improve the overall performance of cloud computing. However, existing studies on cloud workflow scheduling usually consider at most three objectives only and effective methods to solve scheduling problems with four or more objectives still lack. To address the above issue, a new cloud workflow scheduling model is formulated that simultaneously considers four objectives, namely, minimization of makespan, minimization of the average execution time of all workflow instances, maximization of reliability, and minimization of the cost of workflow execution. To solve this four-objective scheduling problem, an improved knee point driven evolutionary algorithm is proposed. Extensive experimental results demonstrate that the improved algorithm outperforms existing popular many-objective evolutionary algorithms in most experimental scenarios studied in this work, in particular when there is sufficiently large amount of computing resource supply and the time for scheduling is limited. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cloud computing Cloud workflow scheduling many-objective optimization problems Knee point driven evolutionary algorithm

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A new uniform evolutionary algorithm based on decomposition and CDAS for many-objective optimization

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KNOWLEDGE-BASED SYSTEMS 2015年第Sep.期85卷 131-142页

作者： Dai Cai Wang Yuping Shaanxi Normal Univ Coll Comp Sci Xian 710062 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

The convergence and the diversity are two main goals of an evolutionary algorithm for many-objective optimization problems. However, achieving these two goals simultaneously is the difficult and challenging work for multi-objective evolutionary algorithms. A uniform evolutionary algorithm based on decomposition and the control of dominance area of solutions (CDAS) is proposed to achieve these two goals. Firstly, a uniform design method is utilized to generate the weight vectors whose distribution is uniform over the design space, then the initial population is classified into some sub-populations by these weight vectors. Secondly, an update strategy based on decomposition is proposed to maintain the diversity of obtained solutions. Thirdly, to improve the convergence, a crossover operator based on the uniform design method is constructed to enhance the search capacity and the CDAS is used to sort solutions of each sub-population to guide the search process to converge the Pareto optimal solutions. Moreover, the proposed algorithm compare with some efficient state-of-the-art algorithms, e.g., NSGAII-CDAS, MOEA/D, UMOEND and HypE, on six benchmark functions with 5-25 objectives are made, and the results indicate that the proposed algorithm is able to obtain solutions with better convergence and diversity. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Decomposition Uniform design Weight vector many-objective optimization problems

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A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization

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APPLIED SOFT COMPUTING 2015年 30卷 238-248页

作者： Dai, Cai Wang, Yuping Shaanxi Normal Univ Coll Comp Sci Xian 710062 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multi-objective optimization Decomposition Uniform design Weight vector many-objective optimization problems

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A reference vector adaptive strategy for balancing diversity and convergence in many-objective evolutionary algorithms

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APPLIED INTELLIGENCE 2023年第7期53卷 7423-7438页

作者： Zhang, Lin Wang, Liping Pan, Xiaotian Qiu, Qicang Zhejiang Univ Technol Coll Comp Sci & Technol Coll Software Hangzhou 310023 Peoples R China Zhejiang Lab Hangzhou 310023 Peoples R China

Decomposition-based multiobjective evolutionary algorithms (MOEA/D) have achieved great success in the field of evolutionary multiobjective optimization, and their outstanding performance in solving for the Pareto-optimal solution set has attracted attention. This type of algorithm uses reference vectors to decompose the multiobjective problem into multiple single-objective problems and searches them collaboratively, hence the choice of reference vectors is particularly important. However, predefined reference vectors may not be suitable for dealing with many-objective optimization problems with complex Pareto fronts (PFs), which can affect the performance of MOEA/D. To solve this problem, we introduce a reference vector initialization strategy, namely, scaling of the reference vectors (SRV), and also propose a new reference vector adaptation strategy, that is, transformation of the solution positions (TSP) based on the ideal point solution, to deal with irregular PFs. The TSP strategy can adaptively redistribute the reference vectors through periodic adjustment to endow that the solution set with better convergence and a better distribution. Both strategies are introduced into a representative MOEA/D, called.-DEA-TSP, which is compared with five state-of-the-art algorithms to verify the effectiveness of the proposed TSP strategy.

关键词： many-objective optimization problems Decomposition Complex Pareto fronts Reference vector adaptation

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A hybrid many-objective cuckoo search algorithm

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SOFT COMPUTING 2019年第21期23卷 10681-10697页

作者： Cui, Zhihua Zhang, Maoqing Wang, Hui Cai, Xingjuan Zhang, Wensheng Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Shanxi Peoples R China Tongji Univ Sch Elect & Informat Shanghai 201804 Peoples R China Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Jiangxi Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Cuckoo search (CS) is an excellent population-based algorithm and has shown promising performance in dealing with single- and multi-objective optimization problems. However, for many-objective optimization problems (MaOPs), CS cannot be directly employed. So far, few paper have been reported to use CS to solve MaOPs. In this paper, we try to propose a hybrid many-objective cuckoo search (HMaOCS) for MaOPs. In HMaOCS, the standard CS is firstly modified to effectively deal with MaOPs. Then, non-dominated sorting and the strategy of reference points are employed to ensure the convergence and diversity. In order to verify the performance of HMaOCS, DTLZ and WFG benchmark sets are utilized in the experiments. Experimental results show that HMaOCS can achieve promising performance compared with five other well-known many-objective optimization algorithms.

关键词： Cuckoo search many-objective optimization problems Non-dominated sorting Reference points

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