Stepladder determinative brain storm optimization

Abstract

Brain Storm Optimization (BSO) is a swarm intelligence algorithm that mimics the brainstorming process of human beings. Since the introduction of BSO, many attempts have been made to improve the performance of the algorithm, mainly with respect to its convergence time and quality of solutions. In this work, we introduce a novel modified version of BSO named Stepladder Determinative BSO (S-DBSO). This algorithm is inspired by a brainstorming technique for decision making, proposed in the field of psychology. This is called Stepladder Technique, and it considers a creative brainstorming process that guarantees the equal participation of all members, even the most introverted of the group, to express their thoughts. The main achievements of this study include: 1) computational modeling of the Stepladder brainstorming process for algorithmic optimization; 2) optimization of the search ability of the algorithm towards the best solutions, after having given the right directionality to the newly generated individuals; 3) better convergence speed with fewer iterations of algorithm required, compared to other state-of-the-art methods and 4) avoidance of being trapped into local optima. Experiments based on well-recognized benchmarks, including the test suite of the competition for single-objective real parameter optimization of the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation (IEEE CEC 2017), validate that S-DBSO outperforms the original and state-of-the-art variations of BSO, as well as other state-of-the-art metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Bat Algorithm (BA). © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.