Comparative study of five bio-inspired evolutionary optimization techniques

Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solutions in optimization problems, for which traditional mathematical techniques may fail. This paper does a comparative study of results of five evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Artificial Bee Colony (ABC) Algorithm, Invasive Weed Optimization (IWO) Algorithm and Artificial Immune (AI) Algorithm when applied to some standard benchmark multivariable functions.