Cellular automata-based multi-objective hybrid grey wolf optimization and particle swarm optimization algorithm for wellbore trajectory optimization

2021 ◽  
Vol 85 ◽  
pp. 103695
Author(s):  
Kallol Biswas ◽  
Pandian M. Vasant ◽  
Jose Antonio Gamez Vintaned ◽  
Junzo Watada
Keyword(s):  
Particle Swarm Optimization ◽  
Cellular Automata ◽  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Multi Objective

scholarly journals Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

2021 ◽  
Author(s):  
Noel Jose Thengappurackal Laiju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

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.

scholarly journals Modeling Hybrid Metaheuristic Optimization Algorithm for Convergence Prediction

2021 ◽  
Author(s):  
Noel Jose Thengappurackal Laiju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Whale Optimization

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.

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