Improved shuffled frog leaping algorithm for continuous optimisation adapted SEVO toolbox

Abstract

This paper presents improved shuffled frog leaping algorithm (ISFLA) with controlled random search behaviour. The work proposes adaptation of random solution generation rule with control parameter to manage the direction of search in conventional SFLA. To evaluate the effectiveness of ISFLA, it has been compared with respect to GA, MA, PSO and SFLA for large dimensions-100, 500 and 1,000 over benchmark test problems using SEVO toolbox. Results depict that ISFLA performs considerably better for all benchmark problems. Results also demonstrated the utility and simplicity of SEVO toolbox for simulating new algorithms. ANOVA test substantiated the statistical significance of the obtained results.