SEVO: Bio-inspired Analytical Tool for Uni-modal and Multimodal Optimization

Abstract

With the rising success of bio-inspired algorithms to solve combinatorial optimization problems, research is focused towards design of new bio-inspired algorithms or new variants of existing algorithms. To validate the reliability of new algorithms with respect to the existing algorithms, they are tested using benchmark test functions. However existing automated tools with benchmark test problems are limited to genetic and evolutionary algorithms only. Therefore large group of researchers have to repeatedly write the same code for the existing algorithms along with their own proposed version. To address this need, the paper presents a unified swarm and evolutionary optimization (SEVO) tool that automates established algorithms like genetic algorithm (GA), memetic algorithm (MA), particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) over sixteen benchmark test functions with diverse properties. SEVO tool provides a user friendly interface to the users for input parameters, options to simultaneously execute any combinations of algorithms and generate graphs for comparison. To substantiate the effectiveness of SEVO tool, experiments were performed to compare the abilities of GA, MA, PSO and SFLA to attain global minima and speed of convergence. Results establish that convergence rate of SFLA is significantly better than PSO, MA and GA. SFLA also outperformed PSO, MA and GA in attaining global minima. Thus SEVO toolbox may serve as an imperative aid for the bio-inspired research community to perform simulations of the embedded algorithms over varied classes of optimizations test problems with minimum time and effort.