Ensemble of the nature-inspired algorithms with success-history based position adaptation

Abstract

Previously, a meta-heuristic approach called Co-Operation of Biology Related Algorithms, or COBRA for short, based on a fuzzy logic controller for solving real-valued optimization problems was introduced and described. The basic idea of the originally proposed approach consists in a cooperative work of six well-known biology-inspired algorithms (components) with similar schemes. Furthermore, the fuzzy logic controller determines which biology-inspired algorithms should be included in the co-operative work and their population sizes at a given moment for solving optimization problems using the COBRA approach. In this study a new modification of the COBRA approach based on an alternative way of generating potential solutions is proposed. The stated technique uses a historical memory of successful positions found by individuals to guide them in different directions and thus to improve their exploration and exploitation abilities. The proposed method was applied to the components of the COBRA approach and to its basic procedures. The modified meta-heuristic as well as other variants of the COBRA algorithm and components (with and without the proposed modification) were evaluated on three sets of low- and high-dimensional benchmark problems. The experimental results obtained by all algorithms are presented and compared. It was concluded that the fuzzy-controlled COBRA with success-history based position adaptation allows better solutions to be found than the other mentioned biology-inspired algorithms with the same computational effort. Thus, the usefulness of the proposed position adaptation technique was demonstrated.

Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms

“…In this study, the idea of applying a success history-based archive of potentially good solutions is implemented for the COBRA-f algorithm. This paper is an extended version of our paper published in the proceedings of the 8th International Workshop on Mathematical Models and their Applications (IWMMA 2019) (Krasnoyarsk, Russian Federation, 18-21 November 2019) [46]. Algorithm introduced in [46] was tested on two additional sets of benchmark functions.…”

“…This paper is an extended version of our paper published in the proceedings of the 8th International Workshop on Mathematical Models and their Applications (IWMMA 2019) (Krasnoyarsk, Russian Federation, 18-21 November 2019) [46]. Algorithm introduced in [46] was tested on two additional sets of benchmark functions. Moreover, population size changes were observed while solving various benchmark problems with 10 and 30 variables.…”