A Modified Whale Optimization Algorithm with Single-Dimensional Swimming for Global Optimization Problems

Abstract

As a novel meta-heuristic algorithm, the Whale Optimization Algorithm (WOA) has well performance in solving optimization problems. However, WOA usually tends to trap in local optimal and it suffers slow convergence speed for large-scale and high-dimension optimization problems. A modified whale optimization algorithm with single-dimensional swimming (abbreviated as SWWOA) is proposed in order to overcome the shortcoming. First, tent map is applied to generate the initialize population for maximize search ability. Second, quasi-opposition learning is adopted after every iteration for further improving the search ability. Third, a novel nonlinearly control parameter factor that is based on logarithm function is presented in order to balance exploration and exploitation. Additionally, the last, single-dimensional swimming is proposed in order to replace the prey behaviour in standard WOA for tuning. The simulation experiments were conducted on 20 well-known benchmark functions. The results show that the proposed SWWOA has better performance in solution precision and higher convergence speed than the comparison methods.

Context-Aware Clustering and the Optimized Whale Optimization Algorithm: An Effective Predictive Model for the Smart Grid

“…As improving the conventional ANN classifier using the optimized bio-inspired algorithm WOA is the key contribution of this paper, researchers have reviewed some recent research articles [40][41][42][43][44][45][46][47] on improved WOA in this section. [40] presented the idea of a leader to direct the populace toward the ideal solution area and quicken WOA convergence.…”

“…In [44], the quantum behavior in the conventional WOA proposed replicating humpback whales' hunting activity to improve the algorithm's search capabilities. To enhance the WOA, a modified WOA with single-dimensional swimming (SWWOA) had presented in [45]. To optimize searchability, the initialized population had generated using a tent map.…”

“…Also, the conventional optimization algorithms failed to overcome the challenges of slow convergence and quickly into local optima for the MDDS dataset. • The improved bio-inspired algorithm WOA had been proposed in research articles [40][41][42][43][44][45][46][47], but none of these methods have been applied to the energy consumption forecasting domain. Except for the research article [47], none of the improved WOA techniques were applied to machine learning algorithms.…”

A modified Whale Optimization Algorithm for exploitation capability and stability enhancement

“…When compared to various other modified versions of the conventional WOA, the proposed technique yielded a superior performance. In [ 20 ], a single dimensional swimming based WOA was proposed. This method was tested on a large range of dimensions, but the minimum dimension was 20.…”

“…While the relevant proposed algorithm succeeded in their objectives, there still exists gaps in application. The authors in [ 18 , 20 ] and [ 22 ] do not consider small dimension magnitudes, and only apply the proposed algorithm to classical benchmark functions (as opposed to modern functions). In [ 21 ], there was no comparison to any other MOT, even the conventional WOA.…”