Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with a crossover strategy

Abstract

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.