FBI inspired meta-optimization

This study developed a novel optimization algorithm, called Forensic-Based Investigation (FBI), inspired by the suspect investigation-location-pursuit process that is used by police officers. Although numerous unwieldy optimization algorithms hamper their usability by requiring predefined operating parameters, FBI is a user-friendly algorithm that does not require predefined operating parameters. The performance of parameter-free FBI was validated using four experiments: (1) The robustness and efficiency of FBI were compared with those of 12 representations of the top leading metaphors by using 50 renowned multidimensional benchmark problems. The result indicated that FBI remarkably outperformed all other algorithms. (2) FBI was applied to solve a resource-constrained scheduling problem associated with a highway construction project. The experiment demonstrated that FBI yielded the shortest schedule with a success rate of 100%, indicating its stability and robustness. (3) FBI was utilized to solve 30 benchmark functions that were most recently presented at the IEEE Congress on Evolutionary Computation (CEC) competition on bound-constrained problems. Its performance was compared with those of the three winners in CEC to validate its effectiveness. (4) FBI solved high-dimensional problems, by increasing the number of dimensions of benchmark functions to 1000. FBI is efficient because it requires a relatively short computational time for solving problems, it reaches the optimal solution more rapidly than other algorithms, and it efficaciously solves high-dimensional problems. Given that the experiments demonstrated FBI's robustness, efficiency, stability, and user-friendliness, FBI is promising for solving various complex problems. Finally, this study provided the scientific community with a metaheuristic optimization platform for graphically and logically manipulating optimization algorithms. (C) 2020 Elsevier B.V. All rights reserved.