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On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

Kamel Zeltni, Souham Meshoul, Heyam H. Al-Baity
Copyright: © 2020 |Pages: 21
ISBN13: 9781799817543|ISBN10: 1799817547|EISBN13: 9781799817550
DOI: 10.4018/978-1-7998-1754-3.ch075
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MLA

Zeltni, Kamel, et al. "On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space." Robotic Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 1573-1593. https://doi.org/10.4018/978-1-7998-1754-3.ch075

APA

Zeltni, K., Meshoul, S., & Al-Baity, H. H. (2020). On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space. In I. Management Association (Ed.), Robotic Systems: Concepts, Methodologies, Tools, and Applications (pp. 1573-1593). IGI Global. https://doi.org/10.4018/978-1-7998-1754-3.ch075

Chicago

Zeltni, Kamel, Souham Meshoul, and Heyam H. Al-Baity. "On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space." In Robotic Systems: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1573-1593. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1754-3.ch075

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Abstract

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

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