Halfway Escape Optimization: A Quantum-Inspired Solution for Complex Optimization Problems
- URL: http://arxiv.org/abs/2405.02850v2
- Date: Mon, 13 May 2024 04:56:36 GMT
- Title: Halfway Escape Optimization: A Quantum-Inspired Solution for Complex Optimization Problems
- Authors: Jiawen Li, Anwar PP Abdul Majeed, Pascal Lefevre,
- Abstract summary: Halfway Escape Optimization (HEO) algorithm is a novel quantum-inspired metaheuristic designed to address complex optimization problems characterized by rugged landscapes and high-dimensionality with an efficient convergence rate.
The study presents a comprehensive comparative evaluation of HEO's performance against established optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (A), Grey Wolf (GWO), and Quantum behaved Particle Swarm Optimization (QPSO)
The simple test of HEO in Traveling Salesman Problem (TSP) also infers its feasibility in real-time applications.
- Score: 6.3816899727206895
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a novel quantum-inspired metaheuristic designed to address complex optimization problems characterized by rugged landscapes and high-dimensionality with an efficient convergence rate. The study presents a comprehensive comparative evaluation of HEO's performance against established optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (AFSA), Grey Wolf Optimizer (GWO), and Quantum behaved Particle Swarm Optimization (QPSO). The primary analysis encompasses 14 benchmark functions with dimension 30, demonstrating HEO's effectiveness and adaptability in navigating complex optimization landscapes and providing valuable insights into its performance. The simple test of HEO in Traveling Salesman Problem (TSP) also infers its feasibility in real-time applications.
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