Benchmarking heuristic search and optimisation algorithms in Matlab

Abstract

With the proliferating development of heuristic methods, it has become challenging to choose the most suitable ones for an application at hand. This paper evaluates the performance of these algorithms available in Matlab, as it is problem dependent and parameter sensitive. Further, the paper attempts to address the challenge that there exists no satisfied benchmarks to evaluation all the algorithms at the same standard. The paper tests five heuristic algorithms in Matlab, the Nelder-Mead simplex search, the Genetic Algorithm, the Genetic Algorithm with elitism, Simulated Annealing and Particle Swarm Optimization, with four widely adopted benchmark problems. The Genetic Algorithm has an overall best performance at optimality and accuracy, while PSO has fast convergence speed when facing unimodal problem.