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

Sina Alizadeh, Malek Mouhoub

2024

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 and exploitation using HdFA and SA. During the third step, the search is limited to the most promising sub-region discovered. Subsequently, the SA algorithm employs the best solution’s information, including its fitness value and position, to efficiently exploit the limited search space. The proposed HdFA-SA technique is then compared against different metaheuristics across ten well-known unimodal and multimodal continuous optimization benchmarks. The results demonstrate HdFA-SA’s exceptional performance in finding the global optima solution while simultaneously reducing execution time.

Paper Citation

in Harvard Style

Alizadeh S. and Mouhoub M. (2024). Enhancing Continuous Optimization with a Hybrid History-Driven Firefly and Simulated Annealing Approach. In Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH; ISBN 978-989-758-708-5, SciTePress, pages 120-127. DOI: 10.5220/0012812900003758

in Bibtex Style

@conference{simultech24,
author={Sina Alizadeh and Malek Mouhoub},
title={Enhancing Continuous Optimization with a Hybrid History-Driven Firefly and Simulated Annealing Approach},
booktitle={Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH},
year={2024},
pages={120-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012812900003758},
isbn={978-989-758-708-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH
TI - Enhancing Continuous Optimization with a Hybrid History-Driven Firefly and Simulated Annealing Approach
SN - 978-989-758-708-5
AU - Alizadeh S.
AU - Mouhoub M.
PY - 2024
SP - 120
EP - 127
DO - 10.5220/0012812900003758
PB - SciTePress