A comparative performance analysis of intelligence-based algorithms for optimizing competitive facility location problems

Most companies operate to maximize profits and increase their market shares in competitive environments. Since the proper location of facilities conditions profits, facility problem (CFLP) has been extensively applied literature. This generally falls within class NP-hard problems, which are difficult solve. Therefore, choosing a solution method optimize is key factor. Even though CFLPs have consistently solved investigated, an important question that keeps being neglected how choose appropriate technique. there no specific criteria for method, reasons behind selection approach mostly unclear. These models using several optimization techniques. As harder-to-solve problems usually meta-heuristics, we apply different meta-heuristic techniques new version CFLP incorporates reliability congestion. We divide algorithms into four categories based on nature meta-heuristics: evolution-based, swarm intelligence-based, physics-based, human-based. GAMS software also solve smaller-size CFLPs. The genetic algorithm differential evolution first category, particle artificial bee colony second, Tabu search harmony third, simulated annealing vibration damping fourth our model. Statistical analyses implemented evaluate compare relative performances. results show third perform better than others.