Swarm Space Hopping Algorithm: A Swarm-based Stochastic Optimizer Enriched with Half Space Hopping Search

Abstract

Many recent swarm-based metaheuristics are trapped in the exploitation of the highest quality as the main or the only reference and the neighbourhood search with the reduction of local search space during the iteration. Regarding to this issue, this paper introduces a novel metaheuristic called swarm space hopping algorithm (SSHA). SSHA consists of three searches. First, a directed search toward the highest quality is performed. Second, the directed search toward the resultant of better agents or away from the other agent is performed. Third, the arithmetic crossover between the agent and a randomized solution in the first half or second half of space is performed. In this work, three evaluations are performed to assess the performance of SSHA. The first evaluation is the benchmark evaluation to compare the performance of SSHA with other recent metaheuristics: northern goshawk optimization (NGO), zebra optimization algorithm (ZOA), clouded leopard optimization (CLO), osprey optimization algorithm (OOA), and total interaction algorithm (TIA). The result exhibits that SSHA is better than NGO, ZOA, CLO, OOA, and TIA in 21,20,17,17,and 21 functions. In the second evaluation, the individual search evaluation to compare the contribution between the first and second searches is performed, with the result that the second search outperforms the first search. The third evaluation is performed to assess the contribution of the third search in the optimization process, and the result shows that the contribution of the third search is significant only in three functions.

Dollmaker Optimization Algorithm: A Novel Human-Inspired Optimizer for Solving Optimization Problems

“…ABC simulates hierarchical behaviors within bee colonies, and FA takes inspiration from the Information exchange through optical communication among fireflies. Natural behaviors among living organisms such as foraging, hunting, migration, digging, flight strategy, and chasing process have been sources of inspiration in designing swarm-based algorithms such as: Pufferfish Optimization Algorithm [16], Grey Wolf Optimizer (GWO) [17], Wombat Optimization Algorithm (WOA) [18], Termite Alate Optimization Algorithm (TAOA) [19], Whale Optimization Algorithm (WOA) [20], African Vultures Optimization Algorithm (AVOA) [21], Swarm Space Hopping Algorithm (SSHA) [22], Reptile Search Algorithm (RSA) [23], Marine Predator Algorithm (MPA) [24], Migration-Crossover Algorithm (MCA) [25], White Shark Optimizer (WSO) [26], and Tunicate Swarm Algorithm (TSA) [27].…”