Fisherman search procedure

Optimization is used in diverse areas of science, technology, and business. Metaheuristics are one of the common approaches for solving optimization problems. In this paper, we propose a novel and functional metaheuristic Fisherman Search Procedure (FSP), to solve global optimization problems, which explores new solutions using a combination of guided and local search. We evaluate the performance of FSP on a set of benchmark functions commonly used for testing global optimization methods. We compare FSP with other heuristic methods referenced in the literature, namely differential evolution (DE), particle swarm optimization (PSO), and greedy randomized adaptive search procedures. Results are analyzed in terms of successful runs, i.e., convergence on global minimum values, and time consumption, demonstrating that FSP can achieve very good performances in most of the cases. In 90 % of the cases, FSP is located among the two better results as for successful runs. On the other hand, with regard to time consumption, FSP shows similar results to PSO and DE, achieving the best and second best results for 82 % of the test functions. Finally, FSP showed to be a simple and robust metaheuristic that achieves good solutions for all evaluated theoretical problems.