Compound Particle Swarm Optimization in Dynamic Environments

Liu, Li‐Li; Wang, Dingwei; Yang, Shengxiang

doi:10.1007/978-3-540-78761-7_67

Cited by 23 publications

(19 citation statements)

References 15 publications

(22 reference statements)

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“…Similarly, the compound concept derived by a branch of physics was introduced in PSO [166,101]. Instead of the "fittest first" principle in SPSO, swarms are constructed based on a "worst first" principle, but each composite particle consists of three fixed particles.…”

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

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472

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

Self Cite

472

213

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“…Liu et al [24] introduced compound particle swarm optimization (CPSO) utilizing a new type of particles which helps explore the search space more comprehensively after a change occurred in the environment. In another work, they used composite particles which help quickly find the promising optima in the search space while maintaining the diversity by a scattering operator [25].…”

Section: Pso In Dynamic Environmentsmentioning

confidence: 99%

A New Particle Swarm Optimization Algorithm for Dynamic Environments

Kamosi

Hashemi

Meybodi

2010

Lecture Notes in Computer Science

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Abstract. Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarms to exploit promising areas found by the parent swarm. To improve the search performance, when the search areas of two child swarms overlap, the worse child swarms will be removed. Moreover, in order to quickly track the changes in the environment, all particles in a child swarm perform a random local search around the best position found by the child swarm after a change in the environment is detected. Experimental results on different dynamic environments modelled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including FMSO, a similar particle swarm algorithm for dynamic environments, for all tested environments.

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“…In DRPBG, there are two tests, one using 10 peaks and the other using 50 peaks. The performance of our algorithm is evaluated in terms of mean and standard deviation (STD) of error values along with DASA [11], jDE [12], CPSO [13], CESO [14], PSO-CP [14]. The initial configuration values of Multi-SAPSO and Multi-SAPSO-PRE are given in Table 2.…”

Section: Experimental Studymentioning

confidence: 99%

Multi-swarm hybrid optimization algorithm with prediction strategy for dynamic optimization problems

Nie¹,

Xu²

2017

Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016)

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Abstract. It is known that optimization in a changing environment is a challenging task, for which the basic goal is not only to obtain the optimal solution, but also strongly adapting to the environmental changes and tracking the optimal solution as closely as possible. In this paper, a novel multi-swarm optimization algorithm is proposed for solving dynamic optimization problems (DOPs) effectively, which is based on the hybrid of particle swarm optimization (PSO) and Simulated Annealing (SA) with an prediction strategy. Firstly, an multi-swarm strategy is adopted, which simultaneously employs PSO method to conduct global search for exploring promising optimal solutions and adopt SA to conduct local search. Secondly, a new forecasting model is developed by using the principle that the previous optimum locations can predict the optimum's location in the changing environment, which can improve the performance of the algorithm in dynamic environment. Then, a diversity preservation mechanism is incorporated into our method to obtain more robust results. Experiments are conducted on the set of benchmark functions used in CEC 2009 competition for DOPs, and the results show that the proposed algorithm achieves good performance and outperforms others in solving DOPs with the model changed by following some pattern.

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Compound Particle Swarm Optimization in Dynamic Environments

Cited by 23 publications

References 15 publications

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

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

A New Particle Swarm Optimization Algorithm for Dynamic Environments

Multi-swarm hybrid optimization algorithm with prediction strategy for dynamic optimization problems

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