Enhancing Continuous Optimization with a Hybrid History-Driven Firefly and Simulated Annealing Approach

Sina Alizadeh, Malek Mouhoub

Abstract: In this study, we propose a hybrid History-driven approach through collaboration between Firefly (FA) and Simulated Annealing (SA) algorithms, to improve the hybrid framework performance in finding the global optima in continuous optimization problems in less time. A Self-Adaptive Binary Space Partitioning (SA-BSP) tree is used to partition the search space of a continuous problem and guide the hybrid framework towards the most promising sub-region. To solve the premature convergence challenge of FA a ”Finder − Tracker agents” mechanism is introduced. The hybrid framework progresses through three main stages. Initially, in the first phase, the SA-BSP tree is utilized within the FA algorithm as a unit of memory. The SA-BSP tree stores significant information of the explored regions of the search space, creates the fitness landscape, and divides the search space during exploration. Moving on to the second phase, a smart controller is introduced to maintain a balance between exploration