Handling Infeasibility in Particle Swarm Optimization Using Constraint Programming

DOI

10.2991/978-94-6463-252-1_42How to use a DOI?

Keywords

Optimization; Meta-heuristic; Particle swarm optimization; Genetic Algorithm; Constraint Programming (CP); feasibility

Abstract

Meta-heuristic approaches, particularly population-based methods, are used to solve NP-Hard problems in the search for near-optimal solutions. In highly combinatorial problems, due to the presence of numerous cons- traints, these population-based methods are expensive in terms of generating feasible solutions for the population across iterations. Several times, it is nearly impossible to get feasible solutions across pop ulations. Therefore, there is a need to reduce the infeasibility and refine the search space for these approaches. In general, meta-heuristic search capability needs to be increased when search space becomes discrete and is tight by constraints. In this paper, work has been done in that direction where search space is sampled from the feasible space prior to the application of Particle Swarm Optimization (PSO) such that it smartly moves in the feasible region and tries to find the optimal solution. The process starts with sampling using constraint programming with linear constraints and then applying a population-based meta-heuristic with a non-linear complex objective/fitness function including, penalizing infeasible solutions. The presented results are a testimony that our method is successful in reducing the infeasible region over the number of iterations.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Robin Ajmera
AU  - Akansha Kumar
AU  - Sai K. Jayakumar
PY  - 2023
DA  - 2023/11/09
TI  - Handling Infeasibility in Particle Swarm Optimization Using Constraint Programming
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 378
EP  - 388
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_42
DO  - 10.2991/978-94-6463-252-1_42
ID  - Ajmera2023
ER  -