Particle Swarms for Dynamic Optimization Problems

Blackwell, Tim; Branke, Jürgen; Li, Xiaodong

doi:10.1007/978-3-540-74089-6_6

Cited by 101 publications

(93 citation statements)

References 21 publications

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“…Li et al [150] integrated the quantum particles (from mQSO) to the SPSO to improve its adaptation capabilities. Thereafter, both SPSO and mQSO were further improved by converting particles to quantum particles for a single iteration after a dynamic change is detected [146]. The quantum principle introduced in [164] was also applied in a speciation FA (SFA) algorithm [179] and in a continuous variation of ACO [190].…”

Section: Maintaining Diversity During Executionmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Maintaining Diversity During Executionmentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

475

213

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“…Offline error of the proposed algorithm, mQSO10(5+5 q ) [5], FMSO [7], cellular PSO [3], and kamosi [9] for different dynamic environment is presented in table 2 to table 6. For each environment, result of the best performing algorithm(s) with 95% confidence is printed in bold.…”

Section: Resultsmentioning

confidence: 99%

“…SPSO [5] distributes the particles dynamically between the types. SPSO is extended based on the theory of the types.…”

Section: Related Workmentioning

confidence: 99%

“…The radius of quantum particles (r q ) is set to 0.5. The proposed algorithm is compared with mQSO [5] and FMSO [7], and cellular PSO [3], and kamosi [9]. For mQSO we adapted a configuration 10(5+5 q ) which creates 10 swarms with 5 neutral (standard) particles and 5 quantum particles with r cloud =0.5 and r excl = r conv =31.5, as suggested in [5].…”

Section: Experimental Settingsmentioning

confidence: 99%

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Adaptive Particle Swarm Optimization Algorithm for Dynamic Environments

Rezazadeh

Meybodi

Naebi

2011

Lecture Notes in Computer Science

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Abstract. Many real world optimization problems are dynamic in which global optimum and local optimum change over time. Particle swarm optimization has performed well to find and track optimum in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes FCM to adapt exclusion radios and utilize a local search on best swarm to accelerate progress of algorithm and adjust inertia weight adaptively. To improve the search performance, when the search areas of two swarms are overlapped, the worse swarms will be removed. Moreover, in order to track quickly the changes in the environment, all particles in the swarm convert to quantum particles when a change in the environment is detected. Experimental results on different dynamic environments modeled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, for all evaluated environments.

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“…Several additional issues must be addressed, including outdated memory, population re-diversification, change detections and response strategies. For further information, readers can refer to [7].…”

Section: Niching Psos For Dynamically Changing Multimodal Environmentsmentioning

confidence: 99%

Developing Niching Algorithms in Particle Swarm Optimization

2011

Adaptation, Learning, and Optimization

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Abstract. Niching as an important technique for multimodal optimization has been used widely in the Evolutionary Computation research community. This chapter aims to provide a survey of some recent efforts in developing stateof-the-art PSO niching algorithms. The chapter first discusses some common issues and difficulties faced when using niching methods, then describe several existing PSO niching algorithms and how they combat these problems by taking advantages of the unique characteristics of PSO. This chapter will also describe a recently proposed lbest ring topology based niching PSO. Our experimental results suggest that this lbest niching PSO compares favourably against some existing PSO niching algorithms.

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Particle Swarms for Dynamic Optimization Problems

Cited by 101 publications

References 21 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Adaptive Particle Swarm Optimization Algorithm for Dynamic Environments

Developing Niching Algorithms in Particle Swarm Optimization

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