Find-Fix-Finish-Exploit-Analyze (F3EA) meta-heuristic algorithm: An effective algorithm with new evolutionary operators for global optimization

Abstract

A novel population-based evolutionary meta-heuristic algorithm is introduced, which imitates the Find-Fix-Finish-Exploit-Analyze (F3EA) targeting process. It considers the surface of the objective function as the battlefield and executes Find-Fix-Finish-Exploit-Analyze steps in an iterative manner. Following the radar detection rationale, a new evolutionary selection operator is introduced during the Find step. It is justified how to model the Fix step as a one-dimensional optimization problem to attain a local search operator. To produce a new solution by the Finish step, the target solution selected in the Find step is actioned artificially. This is an adaptive mutation stage, in which the position of the new potential solution is identified via modeling of projectile motion. The Exploit step takes over opportunities provided by mating the generated solution and its parent solution. Finally, the Analyze step, updates the population. Extensive experiments are conducted based on engineering optimization problems and a large set of benchmark functions for performance assessment, sensitivity analysis of the control parameters, and effectiveness analysis of different steps of the algorithm. Results of statistical tests signify that equipping the algorithm with new selection, mutation and local search operators makes it effective and efficient enough to exceed or match the best of rivals.