Abstract
The project aims at the design and development of six hybrid nature inspired algorithms based on Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), and Binary Bat Optimization Algorithm with Particle Swarm Optimization Algorithm(BATPSO). Hybrid optimizations assume the implementation of two or more algorithms for the same optimization problem. "Hybrid algorithm" does not refer to simply combining multiple algorithms to solve a different problem but rather many algorithms can be considered as combinations of simpler pieces. The hybrid approach combines algorithms that solve the same problem but differs in other characteristics notably performance. A hybrid optimization uses a heuristic to choose the best of these algorithms to apply in a given situation. The proposed hybrid algorithms are benchmarked using a set of 23 classical benchmark functions employed to test different characteristics of hybrid optimization algorithms. The results of the fitness functions prove that the proposed hybrid algorithms are able to produce better or more competitive output with respect to improved exploration, local optima avoidance, exploitation, and convergence. All these hybrid algorithms find superior optimal designs for quintessential engineering problems engaged, showcasing that these algorithms are capable of solving constrained complex problems with diverse search spaces. Optimization results demonstrate that all hybrid algorithms are very competitive compared to the state-of-the-art optimization methods and validated by fitness function. The hybrid algorithms are applied for optimal efficiency determination in various design challenges based on cantilever beam problem.
Highlights
In the recent years, metaheuristic algorithms are employed as primary techniques for obtaining the optimal solutions of real-world engineering design optimization problems [1-3]
Some of the most popular algorithms in this field used in this paper are: Grey Wolf Optimization algorithm(GWO)[5], Artificial Bee Colony Optimization (ABC)[6], Whale Optimization Algorithm (WOA)[7], Moth Flame Optimization Algorithm (MFO)[8], Ant Lion Optimization algorithm (ALO)[9], Cuckoo Search Optimization algorithm (CS)[10], Fire Fly Optimization (FFA)[11], Multi-Verse Optimization algorithm (MVO)[12], Binary Bat Optimization Algorithm (BAT)[13] and Particle Swarm Optimization Algorithm (PSO)[14]
This paper proposes following six hybrid algorithms: Hybrid Grey Wolf Optimization algorithm with Artificial Bee Colony Optimization algorithm (GWOABC), Hybrid Grey Wolf Optimization algorithm with Whale Optimization Algorithm (GWOWOA), Hybrid Moth Flame Optimization Algorithm with Ant Lion Optimization algorithm (MFOALO), Hybrid Cuckoo Search Optimization algorithm with Fire Fly Optimization Algorithm(CSFFA), Hybrid Multi-Verse Optimization algorithm with Particle Swarm Optimization Algorithm (MVOPSO), Hybrid Binary Bat Optimization Algorithm with Particle Swarm Optimization
Summary
Metaheuristic algorithms are employed as primary techniques for obtaining the optimal solutions of real-world engineering design optimization problems [1-3]. An algorithm becomes unique in terms of its characteristics in mixing, allocating or evolving these initial solutions during the optimization process Most of these algorithms take advantages of stochastic operators which makes them unique from deterministic approaches. Meta-heuristic algorithms search for the global optimum in a search space by creating one or more random solutions for a given problem [4]. These algorithms have following advantages: problem independency, evolution independency, local minimum evasion and natural optimization inspirations makes these algorithms makes it simple and follow a general and common framework, which imparts us scope to improve these algorithms with hybridization. Purpose of formulation is to create a mathematical model of the optimal design problem, which can be solved using an optimization algorithm
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