A novel heuristic algorithm: adaptive and various learning-based algorithm

Abstract

A novel population-based heuristic algorithm called the adaptive and various learning-based algorithm (AVLA) is proposed for solving general optimization problems in this paper. The main idea of AVLA is inspired by the learning behaviors of individuals in a group, e.g. a school class. The algorithm formulates the following learning behaviors: a. Elite members will learn from each other; b. A common member will learn from some elite member and other common members; c. Members with unsatisfied performance will reflect their behavior after performance estimation; d. The whole group will reflect their behavior and try to improve if the performance of the group as a whole has not been improved for a long time. AVLA adopts the success-history based parameter adaptation to lighten the burden of parameter adjustment. To verify the efficiency of the AVLA, we apply it and its no-adaptation version with other eight well-known heuristics to 100 benchmark problems. The comparison clearly shows that AVLA performs as well as SHADE and the non-adaption version of AVLA outperforms others except AVLA and SHADE.