A New Optimization Idea: Parallel Search-Based Golden Jackal Algorithm

Abstract

As the variety and scope of optimization problems continue to expand, the applicability of a single algorithm may not be universal. To address this challenge, this paper introduces an enhanced iteration of the Golden Jackal Optimization (GJO) algorithm, termed the Parallel Search-based Golden Jackal Optimization (PGJO) algorithm. This algorithm integrates the concept of parallel search into the initialization, updating, and selection mechanisms. It includes a chaotic mapping preselection during the initialization phase, employs a cloning-like strategy for population updates, and incorporates an advanced simulated annealing approach during the selection phase. Through a comprehensive comparison of PGJO with various intelligent optimization algorithms across diverse benchmark functions and real-world scenarios, we affirm its effectiveness in terms of enhanced convergence accuracy and reduced required iterations.