Abstract
This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems.
Highlights
Computational intelligence [1] is a sub-branch of artificial intelligence which employs a variety of mechanisms to solve complex problems in different domains
Uni-modal benchmark functions have a single global optimum. They are suitable for assessing the effectiveness of the search process of any optimization algorithm when conducting the exploitation phase, while multi-modal benchmark functions are favoured for assessing the capability of an optimization method to explore the search space
The main reason accounting for the high performance of the Velocity-Aided Grey Wolf Optimizer (VAGWO) on such benchmark functions can be summarized in preserving the trajectory of the search agents as the main effect of incorporating the agents’ velocity into the updating procedure, and further strengthening the exploration capability of the proposal by increasing the acceleration coefficients represented by parameter A at the exploration phase
Summary
Computational intelligence [1] is a sub-branch of artificial intelligence which employs a variety of mechanisms to solve complex problems in different domains. The GWO algorithm applies the same mechanism and follows the pack hierarchy for assigning different roles to each member of the pack to reach the food depending on its potential fitness and its rank in the pack. Beta, and delta wolves are rated as the guide wolves in GWO, the omega wolves always follow the location of these guides when searching for food in nature. The three high-fitness wolves are assumed as those with the best locations in the population and are named the alpha, beta, and delta solutions, and the other wolves’ positions are updated according to their distance from the leading solutions to bring about the potentially better agents to be determined within the searching process. The GWO employs effective operators to conduct the search process such that a safe and reliable exploration–exploitation balance could be maintained to avoid premature convergence [10]
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