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10th International Conference on Swarm Intelligence (ICSI)

作者： Niu, Ben Liu, Qianying Wang, Jun Shenzhen Univ Coll Management Shenzhen 518060 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China

ISBN: (纸本)9783030263683;9783030263690

dynamic optimization problems (DOPs) are prevailingly addressed because of their origins from real-world issues. In addition to existing methods that have been developed for evolutionary algorithms to solve DOPs, this paper provides a hybrid memory and clone scheme, called memory-based clone selection, for bacterial foraging optimization algorithms in dynamic environments. Meanwhile, two gene libraries (Random and Non-random) are involved to clone outstanding individuals and dynamically manage the gene hall and memory. This approach not only results in greater diversity and better global search ability, but also enables the algorithm higher adaptability to environmental dynamics and changes. The simulation result generated by a dynamic rotation peak benchmark confirms that proposed memory and clone schemes-based BFO (MCBFO) outperforms standard BFO and PSO in terms of population diversity, convergence rate and searching ability.

关键词： Bacterial foraging optimization dynamic optimization problems Memory Immigrants Clone selection

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Using Heuristic Rules to Enhance a Multiswarm PSO for dynamic Environments

Using Heuristic Rules to Enhance a Multiswarm PSO for Dynami...

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2010 IEEE World Congress on Computational Intelligence

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

ISBN: (纸本)9781424481262

The Particle Swarm optimization (PSO) algorithm has been successfully applied to dynamic optimization problems with very competitive results. One of its best performing variants, the mQSO is based on an atomic model, with quantum and trajectory particles. This work introduces a new version of this algorithm which uses heuristic rules for improving its performance. Two new rules are presented: one specifically designed for the mQSO, which locally bursts diversity after a change in the environment, and a second, more general one, which globally increases diversity in a precise way, without disturbing the intensification of the search. The new version with rules is tested against the original one using several variations of the Moving Peaks Benchmark and the Ackley function. The results show a drastic improvement in the performance of the algorithm.

关键词： Ackley function PSO algorithm dynamic environments dynamic optimization problems heuristic rules locally bursts diversity mQSO moving peaks benchmark multiswarm PSO particle swarm optimisation particle swarm optimization algorithm quantum computing quantum particles trajectory particles

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The Parallel Single Front Genetic Algorithm (PSFGA) in dynamic multi-objective optimization

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9th International Work-Conference on Artificial Neural Networks

作者： Camara, Mario Ortega, Julio De Toro, Francisco Univ Granada ETSIIT Dept Commun Architecture & Technol Granada Spain Univ Granada ETSIIT Dept Signals Telemat & Commun Granada Spain

ISBN: (纸本)9783540730064

This paper analyzes the use of the, previously proposed, Parallel Single Front Genetic Algorithm (PSFGA) in applications in which the objective functions, the restrictions, and hence also solutions can change over the time. These dynamic optimization problems appear in quite different real applications with relevant socio-economic impacts. PSFGA uses a master process that distributes the population among the processors in the system (that evolve their corresponding solutions according to an island model), and collects and adjusts the set of local Pareto fronts found by each processor (this way, the master also allows an implicit communication among islands). The procedure exclusively uses non-dominated individuals for the selection and variation, and maintains the diversity of the approximation to the Pareto front by using a strategy based on a crowding distance.

关键词： dynamic optimization problems parallel evolutionary computation single front multi-objective optimization parallel processing

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A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

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SENSORS 2021年第5期21卷 1903-1903页

作者： de Castro, Edwards Cerqueira Salles, Evandro Ottoni Teatini Ciarelli, Patrick Marques Univ Fed Espirito Santo Programa Posgrad Engn Eletr Ctr Tecnol BR-29075910 Vitoria ES Brazil

This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

关键词： swarm intelligence meta-heuristic dynamic optimization problems video target tracking time series forecasts

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A Cluster-Based Differential Evolution Algorithm With External Archive for optimization in dynamic Environments

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IEEE TRANSACTIONS ON CYBERNETICS 2013年第3期43卷 881-897页

作者： Halder, Udit Das, Swagatam Maity, Dipankar Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

This paper presents a Cluster-based dynamic Differential Evolution with external Archive (CDDE_Ar) for global optimization in dynamic fitness landscape. The algorithm uses a multipopulation method where the entire population is partitioned into several clusters according to the spatial locations of the trial solutions. The clusters are evolved separately using a standard differential evolution algorithm. The number of clusters is an adaptive parameter, and its value is updated after a certain number of iterations. Accordingly, the total population is redistributed into a new number of clusters. In this way, a certain sharing of information occurs periodically during the optimization process. The performance of CDDE_Ar is compared with six state-of-the-art dynamic optimizers over the moving peaks benchmark problems and dynamic optimization problem (DOP) benchmarks generated with the generalized-dynamic-benchmark-generator system for the competition and special session on dynamic optimization held under the 2009 IEEE Congress on Evolutionary Computation. Experimental results indicate that CDDE_Ar can enjoy a statistically superior performance on a wide range of DOPs in comparison to some of the best known dynamic evolutionary optimizers.

关键词： Clustering differential evolution (DE) dynamic optimization problems evolutionary algorithms (EAs) self-adaptation

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An affinity propagation clustering based particle swarm optimizer for dynamic optimization

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KNOWLEDGE-BASED SYSTEMS 2020年 195卷 105711-105711页

作者： Liu, Yuanchao Liu, Jianchang Jin, Yaochu Li, Fei Zheng, Tianzi Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Anhui Univ Technol Dept Elect & Informat Engn Maanshan Anhui Peoples R China

Multipopulation methods, which can enhance the population diversity, are well suited for dynamic optimization. However, there are still some challenges need to be tackled when multipopulation methods are employed, namely, how to avoid sensitive parameters when creating sub-populations, and how to effectively adapt to the changing optima continuously during the search process. Therefore, a novel multipopulation algorithm based on the affinity propagation clustering is proposed to address the above challenges. In the proposed method, affinity propagation clustering is applied for automatically creating sub-populations by message-passing process, which can avoid some extra parameters. Moreover, a simple but effective strategy, denoted as optimal particles relocation, is proposed for responding to environmental changes. In this strategy, the best particles in each sub-population are first stored in a memory. Then, local search is applied for helping the memory to quickly locate new peaks, if the environmental change has occurred. To validate the performance of the proposed algorithm, a variety of experiments have been conducted. The experimental results have demonstrated that the proposed algorithm performs robustly and competitively under different environments. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Affinity propagation clustering Optimal particles relocation dynamic optimization problems Particle swarm optimizer

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A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments

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APPLIED SOFT COMPUTING 2020年第0期88卷 106009-000页

作者： Vafashoar, Reza Meybodi, Mohammad Reza Amirkabir Univ Technol Soft Comp Lab Comp Engn & Informat Technol Dept Tehran Iran

This paper presents a multi-population differential evolution algorithm to address dynamic optimization problems. In the proposed approach, a cellular learning automaton adjusts the behavior of each subpopulation by adaptively controlling its updating schemes. As the environment changes over time, an evolving population may go through a quite number of state transitions. Each state demands specific characteristics from an optimizer;hence, an adapted evolutionary scheme for one state may be unsuitable for the upcoming ones. Additionally, a learning approach may have limited time to adapt to a newly encountered state due to the frequentness of the environmental changes. Hence, it is infeasible for a dynamic optimizer to unlearn its existing beliefs to accommodate the practices required to embrace newly encountered states. In order to address this issue, we introduce a context dependent learning approach, which can adapt the behavior of each subpopulation according to the contexts of its different states. The performance of the proposed approach is compared with several state-of-the-art dynamic optimizers over the GDBG benchmark set. Comparison results indicate that the proposed method can achieve statistically superior performances on a wide range of tested instances. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Differential evolution dynamic optimization problems Hyper-heuristics Cellular learning automata Learning automata

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A fast density peak clustering based particle swarm optimizer for dynamic optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 236卷

作者： Li, Fei Yue, Qiang Liu, Yuanchao Ouyang, Haibin Gu, Fangqing Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China Anhui Univ Technol Anhui Prov Engn Lab Intelligent Demolit Equipment Maanshan 243032 Peoples R China Anhui Univ Technol Anhui Prov Key Lab Special Heavy Load Robot Maanshan 243032 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangzhou 510520 Guangdong Peoples R China

dynamic optimization problems (DOPs) are optimization problems with time evolution characteristics. In this type of problem, the decision variables and the state variables change over time and produce results that will impact the future. To effectively address DOPs, this paper proposes a fast density peak clustering based particle swarm optimizer for dynamic optimization (DPCPSO). The main innovations of DPCPSO contain three critical components. First, a fast density peak clustering is applied to create multiple sub-populations, which can help the algorithm locate peaks. Second, stagnation detection is used to tackle the loss of diversity. Third, an optimal particle calibration strategy which can find the optimal solution quickly in a changing environment is proposed in response to environmental changes. Moreover, the hill climbing method is applied to help the memory quickly locate new peaks if the environment changes. The performance of our proposed algorithm has been tested on Mobile Peak Benchmark (MPB), Generalized dynamic Benchmark Generator (GDBG) and Generalized Moving Peaks Benchmark (GMPB) problems and compared with seven state-of-the-art dynamic optimization algorithms. The experimental results validate the proposed algorithm performed competitively while solving DOPs.

关键词： Fast density peak clustering Particle swarm optimizer Optimal particle calibration strategy dynamic optimization problems

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Dual-archive-based particle swarm optimization for dynamic optimization

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APPLIED SOFT COMPUTING 2019年 85卷 105876-000页

作者： Liu, Xiao-Fang Zhou, Yu-Ren Yu, Xue Lin, Ying Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Dept Psychol Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou Guangdong Peoples R China

In dynamic optimization problems, although the problem environments keep changing, a new environment is usually related to its previous environments. Based on the relevance, the search experience in the previous environments can be reused to accelerate the optimization of the current new environment. Thus, in this paper, we propose a dual-archive-based particle swarm optimization to utilize the useful information accumulated in past environments as well as to explore the emerging information of each new environment. Specifically, in the proposed algorithm, the good solutions found in past environments are stored in two different archives, i.e., a fine-grained archive and a coarsegrained archive, so as to preserve both detailed information and systemic information, respectively. Once the environment is changed, the solutions in the two archives will be used for guidance to quickly find high-quality solutions in the new environment. The proposed algorithm is evaluated on the famous moving peaks benchmark in terms of two performance measures. The experimental results show that the proposed algorithm is competitive with state-of-the-art algorithms for dynamic optimization problems. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization dynamic optimization problems Evolutionary computation Information reuse

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An Improved Univariate Marginal Distribution Algorithm for dynamic optimization Problem

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AASRI Procedia 2012年 1卷 166-170页

作者： Wu Yan Liu Xiaoxiong Department of Mathematics Xidian University Xi’an 710071 China School of Automation Northwestern Polytechnical University Xi’an 710072 China

In dynamic environments, it is difficult to track a changing optimal solution over time. An improved univariate marginal distribution algorithm (IUMDA) is proposed to deal with dynamic optimization problems. This approach is composed of the diffusion model, which uses the information of current population, and the inertia model, which uses the part history information of the optimal solution. After an environment changed, the strategy is changed by a detecting operator to guide increasing the population diversity. Finally an experimental study on dynamic sphere function was carried out to compare the performance of IUMDA and mutation UMDA. The experimental results show that the IUMDA is effective for the function with moving optimum and can adapt the dynamic environments rapidly.

关键词： Evolutionary algorithm Univariate marginal distribution algorithm (UMDA) dynamic optimization problems diversity of population

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