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Particle swarm optimization based on particle perturbation and elite preservation strategies for dynamic optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 279卷

作者： Li, Fei Pan, Hao Ji, Yilong Ouyang, Haibin Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China Anhui Univ Technol Anhui Key Lab Low Carbon Met & Solid Waste Resourc Maanshan 243032 Peoples R China Anhui Prov Engn Lab Intelligent Demolit Equipment Maanshan 243032 Peoples R China Anhui Key Lab Power Elect & Mot Control Maanshan 243032 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 51006 Peoples R China

Multi-population methods have an effective ability to solve various dynamic optimization problems. However, most multi-population methods still have much room for improvement in their global search capabilities. To address this issue, a particle swarm optimization based on particle perturbation and elite preservation strategies (PSO-PE) is designed to solve dynamic optimization problems. The particle perturbation strategy is first designed to enable the optimal particles of each explorer sub-population to escape from local optima. Secondly, the elite preservation strategy is proposed to prevent multiple sub-populations from converging on the same peak by preserving elite particles while excluding others. Finally, an elite particle migration strategy is proposed to cope with environmental changes. Specifically, the exploiter sub-populations can be used to trace the optimal solution in a new environment while the explorer sub-populations are adopted to maintain the diversity. We conduct the experiment on the Moving Peak Benchmark (MPB) and Generalized Moving Peaks Benchmark (GMPB) problems. The comprehensive results have demonstrated the superior performance of the proposed method compared with some related dynamic optimization algorithms.

关键词： dynamic optimization problems Particle swarm optimization Particle perturbation strategy Elite preservation strategy Elite particle migration strategy

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An algorithm comparison for dynamic optimization problems

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APPLIED SOFT COMPUTING 2012年第10期12卷 3176-3192页

作者： del Amo, Ignacio G. Pelta, David A. Gonzalez, Juan R. Masegosa, Antonio D. Univ Granada Models Decis & Optimizat Res Grp MODO Dept Comp Sci & Artificial Intelligence ICT Res Ctr CITIC UGR E-18071 Granada Spain

This work presents a study on the performance of several algorithms on different continuous dynamic optimization problems. Eight algorithms have been used: SORIGA (an Evolutionary Algorithm), an agents-based algorithm, the mQSO (a widely used multi-population PSO) as well as three heuristic-rule-based variations of it, and two trajectory-based cooperative strategies. The algorithms have been tested on the Moving Peaks Benchmark and the dynamic version of the Ackley, Griewank and Rastrigin functions. For each problem, a wide variety of configuration variations have been used, emphasizing the influence of dynamism, and using a full-factorial experimental design. The results give an interesting overview of the properties of the algorithms and their applicability, and provide useful hints to face new problems of this type with the best algorithmic approach. Additionally, a recently introduced methodology for comparing a high number of experimental results in a graphical way is used. (C) 2012 Elsevier B.V. All rights reserved.

关键词： dynamic optimization problems Evolutionary Algorithms PSO Multi-agents Cooperative strategies MPB Ackley Griewank Rastrigin

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Scaling Up dynamic optimization problems: A Divide-and-Conquer Approach

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2020年第1期24卷 1-15页

作者： Yazdani, Danial Omidvar, Mohammad Nabi Branke, Juergen Trung Thanh Nguyen Yao, Xin Southern Univ Sci & Technol Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Shenzhen 518055 Peoples R China Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT W Midlands England Univ Warwick Warwick Business Sch Operat Res & Management Sci Grp Coventry CV4 7AL W Midlands England Liverpool John Moores Univ Dept Maritime & Mech Engn Liverpool L3 3AF Merseyside England

Scalability is a crucial aspect of designing efficient algorithms. Despite their prevalence, large-scale dynamic optimization problems are not well studied in the literature. This paper is concerned with designing benchmarks and frameworks for the study of large-scale dynamic optimization problems. We start by a formal analysis of the moving peaks benchmark (MPB) and show its nonseparable nature irrespective of its number of peaks. We then propose a composite MPB suite with exploitable modularity covering a wide range of scalable partially separable functions suitable for the study of large-scale dynamic optimization problems. The benchmark exhibits modularity, heterogeneity, and imbalance features to resemble real-world problems. To deal with the intricacies of large-scale dynamic optimization problems, we propose a decomposition-based coevolutionary framework which breaks a large-scale dynamic optimization problem into a set of lower-dimensional components. A novel aspect of the framework is its efficient bi-level resource allocation mechanism which controls the budget assignment to components and the populations responsible for tracking multiple moving optima. Based on a comprehensive empirical study on a wide range of large-scale dynamic optimization problems with up to 200-D, we show the crucial role of problem decomposition and resource allocation in dealing with these problems. The experimental results clearly show the superiority of the proposed framework over three other approaches in solving large-scale dynamic optimization problems.

关键词： optimization Heuristic algorithms Benchmark testing Resource management Power system dynamics Sociology Statistics Computational resource allocation cooperative coevolutionary (CC) decomposition dynamic optimization problems large-scale optimization problems multipopulation

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Algorithm portfolio based scheme for dynamic optimization problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2015年第4期8卷 667-689页

作者： Fajardo Calderin, Jenny Masegosa, Antonio D. Pelta, David A. Polytech Higher Inst Jose A Echeverria Dept Artificial Intelligence & Infrastruct & Syst Havana Cuba Univ Deusto Deusto Inst Technol Bilbao Spain Basque Fdn Sci Ikerbasque Bilbao Spain Univ Granada Dept Comp Sci & AI Res Ctr ICT CITIC UGR Granada Spain

Since their first appearance in 1997 in the prestigious journal Science, algorithm portfolios have become a popular approach to solve static problems. Nevertheless and despite that success, they have not received much attention in dynamic optimization problems (DOPs). In this work, we aim at showing these methods as a powerful tool to solve combinatorial DOPs. To this end, we propose a new algorithm portfolio for this type of problems that incorporates a learning scheme to select, among the metaheuristics that compose it, the most appropriate solver or solvers for each problem, configuration and search stage. This method was tested over 5 binary-coded problems (dynamic variants of OneMax, Plateau, RoyalRoad, Deceptive and Knapsack) and compared versus two reference algorithms for these problems (Adaptive Hill Climbing Memetic Algorithm and Self Organized Random Immigrants Genetic Algorithm). The results showed the importance of a good design of the learning scheme, the superiority of the algorithm portfolio against the isolated version of the metaheuristics that integrate it, and the competitiveness of its performance versus the reference algorithms.

关键词： learning algorithm portfolio algorithm selection problem combinatorial problems dynamic optimization problems

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A multi-population harmony search algorithm with external archive for dynamic optimization problems

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INFORMATION SCIENCES 2014年 272卷 84-95页

作者： Turky, Ayad Mashaan Abdullah, Salwani Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Data Min & Optimisat Res Grp Bangi 43600 Selangor Malaysia Swinburne Univ Technol Fac Informat & Commun Technol Hawthorn Vic 3122 Australia

dynamic optimization problems present great challenges to the research community because their parameters are either revealed or changed during the course of an ongoing optimization process. These problems are more challenging than static problems in real-world applications because the latter are usually dynamic, with the environment constantly subjected to change or the size of a problem increasing sporadically. In solving dynamic optimization problems in the real world, proposed solutions should be able to monitor the movement of the optimal point and the changes in the landscape solutions. In this paper, a multi-population harmony search algorithm with external archive for dynamic optimization problems is proposed. Harmony search algorithm is a population-based meta-heuristic optimization technique that is similar to a musical process when a musician is attempting to find a state of harmony. To tackle the problem of dynamism, the population of solutions is divided into several sub-populations such that each sub-population takes charge exploring or exploiting the search space. To enhance the algorithm performance further, an external archive is used to save the best solutions for later use. These solutions will then be used to replace redundant solutions in the harmony memory. The proposed algorithm is tested on the Moving Peak Benchmark. Empirical results show that the proposed algorithm produces better results than several of the current state-of-the-art algorithms. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Multi-population Harmony search algorithm dynamic optimization problems External archive

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An Adaptive Multi-Swarm Optimizer for dynamic optimization problems

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EVOLUTIONARY COMPUTATION 2014年第4期22卷 559-594页

作者： Li, Changhe Yang, Shengxiang Yang, Ming China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China De Montfort Univ Sch Comp Sci & Informat Ctr Computat Intelligence Leicester LE1 9BH Leics England

The multipopulation method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take these challenging issues into account in designing such adaptive algorithms. To address the issues when multipopulation methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multipopulation methods from different research areas in the literature of evolutionary computation.

关键词： Multipopulation adaptation dynamic optimization problems particle swarm optimization

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Multiple landscape measure-based approach for dynamic optimization problems

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 88卷

作者： Li, Kangjing Elsayed, Saber Sarker, Ruhul Essam, Daryl Univ New South Wales Sch Syst & Comp Northcott Dr Canberra ACT 2600 Australia

Many practical decision -making problems involve dynamic scenarios, where the decision variables, conditions and/or parameters of their optimization models change over time. Such problems are known as dynamic optimization problems (DOPs). Although evolutionary algorithms (EAs) have been effective in solving static optimization problems, they face challenges in handling DOPs. To improve the performance of EAs in dealing with DOPs, this paper proposes a new evolutionary framework that uses different landscape measures to analyze problem landscapes and utilizes the information gained from this to improve the searching process. The proposed method is adopted in three EAs to deal with dynamic functions from IEEE-CEC2009 and two real -world problems. According to the experimental results, LIDOA with multi -measure methods improves the performance of GA, jDE and CMA-ES, on average by 6.71%, 3.03% and 7.78% on benchmark problems, respectively.

关键词： dynamic optimization problems Evolutionary algorithms Fitness landscape analysis

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An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems

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APPLIED INTELLIGENCE 2018年第1期48卷 97-117页

作者： Kordestani, Javidan Kazemi Firouzjaee, Hossein Abedi Meybodi, Mohammad Reza Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Soft Comp Lab 424 Hafez Ave Tehran Iran

This paper presents an adaptive bi-flight cuckoo search algorithm for continuous dynamic optimization problems. Unlike the standard cuckoo search which relies on Levy flight, the proposed method uses two types of flight that are chosen adaptively by a learning automaton to control the global and local search ability of the method during the run. Furthermore, a variable nest scheme and a new cuckoo addition mechanism are introduced. A greedy local search method is also integrated to refine the best found solution. An extensive set of experiments is conducted on a variety of dynamic environments generated by the moving peaks benchmark, to evaluate the performance of the proposed approach. Results are also compared with those of other state-of-the-art algorithms from the literature. The experimental results indicate the effectiveness of the proposed approach.

关键词： dynamic optimization problems Moving peaks benchmark DOPs MPB Cuckoo search Learning automata

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A hyper-heuristic based framework for dynamic optimization problems

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APPLIED SOFT COMPUTING 2014年 19卷 236-251页

作者： Topcuoglu, Haluk Rahmi Ucar, Abdulvahid Altin, Lokman Marmara Univ Dept Comp Engn TR-34722 Istanbul Turkey

Most of the real world problems have dynamic characteristics, where one or more elements of the underlying model for a given problem including the objective, constraints or even environmental parameters may change over time. Hyper-heuristics are problem-independent meta-heuristic techniques that are automating the process of selecting and generating multiple low-level heuristics to solve static combinatorial optimization problems. In this paper, we present a novel hybrid strategy for applicability of hyper-heuristic techniques on dynamic environments by integrating them with the memory/search algorithm. The memory/search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems. We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark. The former problem is extended from the generalized assignment problem by changing resource consumptions, capacity constraints and costs of jobs over time;and the latter one is a well-known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation performed on various instances of the given two problems validates that our hyper-heuristic integrated framework significantly outperforms the memory/search algorithm. (C) 2014 Elsevier B.V. All rights reserved.

关键词： dynamic optimization problems Hyper-heuristics Generalized assignment problem Moving peaks benchmark Memory search technique

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The role of cardinality and neighborhood sampling strategy in agent-based cooperative strategies for dynamic optimization problems

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APPLIED SOFT COMPUTING 2014年第PART C期14卷 577-593页

作者： Masegosa, Antonio D. Pelta, David del Amo, Ignacio G. Univ Granada Ctr Res ICT Models Decis & Optimisat Res Grp E-18071 Granada Spain

The best performing methods for dynamic optimization problems (DOPs) are usually based on a set of agents that can have different complexity (like solutions in Evolutionary Algorithms, particles in Particle Swarm optimization, or metaheuristics in hybrid cooperative strategies). While methods based on low complexity agents are widely applied in DOPs, the use of more "intelligent" agents has rarely been explored. This work focuses on this topic and more specifically on the use of cooperative strategies composed by trajectory-based search agents for DOPs. Within this context, we analyze the influence of the number of agents (cardinality) and their neighborhood sampling strategy on the performance of these methods. Using a low number of agents with distinct neighborhood sampling strategies shows the best results. This method is then compared versus state-of-the-art algorithms using as test bed the well-known Moving Peaks Benchmark and dynamic versions of the Ackley's, Griewank's and Rastrigin's functions. The results show that this configuration of the cooperative strategy is competitive with respect to the state-of-the-art methods. (C) 2013 Elsevier B.V. All rights reserved.

关键词： dynamic optimization problems Agent-based optimization Hybrid metaheuristics Cooperative strategies

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