Abstract
Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy’s flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy’s flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.
Highlights
To solve complex optimization problems in an acceptable time, different algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) as well as their variations have been proposed in the last few decades
To improve the Wolf Pack Algorithm (WPA) method in a comprehensive way, in this work, we introduce an optimized approach called OGL-WPA, i.e., an improved WPA based on the Opposition-based learning and Genetic algorithm with Levy’s flight
To improve the performance of WPA, we introduce an optimized approach called OGL-WPA, with the seamless integration of three intelligent techniques: opposition-based learning, Genetic algorithm and Levy’s flight
Summary
With the rapid growth of the complexity of data applications and systems, swarm intelligence has received increasing attentions. To solve complex optimization problems in an acceptable time, different algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) as well as their variations have been proposed in the last few decades. All the approaches can be broadly decomposed into a population initialization stage followed by an iteration computing process, and they have been widely used on handling numerical and real-world optimization problems in various domains (e.g., engineering and computer systems). The Wolf Pack Algorithm (WPA) is a typical swarm intelligence algorithm based on the living habits of wolves [1, 2]. WPA includes three main intelligent behaviors (i.e., wolf’s searching behavior, leader wolf’s calling behavior and fierce wolf’s round-up behavior).
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