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Fitness Predator Optimizer to avoid premature convergence for multimodal problems

A major problem with most of swarm intelligent algorithms in multimodal optimization is premature convergence (PC), which results in great performance loss and sub-optimal solutions. To avoid premature convergence by maintaining diversity in the population, many kinds of optimization algorithms are proposed. However, to the best of our knowledge, few of the swarm intelligent techniques focus on the individual competition. The development of individual competition plays an important role of the diversity conservation in the population because it could increase individual independent consciousness and reduce the rapid social collaboration process. In this paper, a new algorithm, Fitness Predator Optimizer (FPO), is proposed based on the conceptions of predators. In an FPO system, all of the individuals are seen as predators. Each of the individuals is depicted only by its position. Then the individual is named as a “position” in FPO. Only the competitive, powerful positions selected as elites could achieve the limited opportunity to update. The elite team reduces the possibility of all of the individuals moving toward the same place. Eight well-known benchmark functions are used to test the performance of FPO. Four typical multimodal benchmark functions are used to test the global search ability of FPO and four fixed-dimension multimodal optimization problems are selected to make a comparison of the convergence rate between several well-known algorithms. The experimental results show that the FPO algorithm is able to provide excellent exploitation, utilizing local minima avoidance and exploration simultaneously.