A Feature-Based Comparison of Evolutionary Computing Techniques for\n Constrained Continuous Optimisation

2015/09/23 by Shayan Poursoltan, Frank Neumann, Poursoltan, Shayan +1
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.1509.06842

openalex publication_date 2015/09/23 · openalex created_date 2022/10/05 · openalex updated_date 2026/07/28

Abstract

Evolutionary algorithms have been frequently applied to constrained\ncontinuous optimisation problems. We carry out feature based comparisons of\ndifferent types of evolutionary algorithms such as evolution strategies,\ndifferential evolution and particle swarm optimisation for constrained\ncontinuous optimisation. In our study, we examine how sets of constraints\ninfluence the difficulty of obtaining close to optimal solutions. Using a\nmulti-objective approach, we evolve constrained continuous problems having a\nset of linear and/or quadratic constraints where the different evolutionary\napproaches show a significant difference in performance. Afterwards, we discuss\nthe features of the constraints that exhibit a difference in performance of the\ndifferent evolutionary approaches under consideration.\n

Related

• A Feature-Based Prediction Model of Algorithm Selection for Constrained Continuous Optimisation
• A Feature-Based Analysis on the Impact of Set of Constraints for e-Constrained Differential Evolution
• Particle Swarm Optimization: Development of a General-Purpose Optimizer
• Between theory and practice: guidelines for an optimization scheme with genetic algorithms - Part I: single-objective continuous global optimization
• A Review on Constraint Handling Techniques for Population-based Algorithms: from single-objective to multi-objective optimization