Novel memetic of beluga whale optimization with self-adaptive exploration-exploitation balance for global optimization and engineering problems

• Published in: Soft Computing
• Main Authors: Hussien, Abdelazim, Abu Khurma, Ruba, Alzaqebah, Abdullah, Amin, Mohamed, Hashim, Fatma A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Linköpings universitet, Programvara och system 2023
• Subjects: Beluga Whale Optimization; BWO; Elite Evolution Strategy; Self-adaptive exploration-exploitation; Engineering problems; Computer Sciences; Datavetenskap (datalogi); Beluga; Beluga whale; Beluga*
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-195768; https://doi.org/10.1007/s00500-023-08468-3

A population-based optimizer called beluga whale optimization (BWO) depicts behavioral patterns of water aerobics, foraging, and diving whales. BWO runs effectively, nevertheless it retains numerous of deficiencies that has to be strengthened. Premature convergence and a disparity between exploitation and exploration are some of these challenges. Furthermore, the absence of a transfer parameter in the typical BWO when moving from the exploration phase to the exploitation phase has a direct impact on the algorithms performance. This work proposes a novel modified BWO (mBWO) optimizer that incorporates an elite evolution strategy, a randomization control factor, and a transition factor between exploitation and exploitation. The elite strategy preserves the top candidates for the subsequent generation so it helps generate effective solutions with meaningful differences between them to prevent settling into local maxima. The elite random mutation improves the search strategy and offers a more crucial exploration ability that prevents stagnation in the local optimum. The mBWO incorporates a controlling factor to direct the algorithm away from the local optima region during the randomization phase of the BWO. Gaussian local mutation (GM) acts on the initial position vector to produce a new location. Because of this, the majority of altered operators are scattered close to the original position, which is comparable to carrying out a local search in a small region. The original method can now depart the local optimal zone because to this modification, which also increases the optimizers optimization precision control randomization traverses the search space using random placements, which can lead to stagnation in the local optimal zone. Transition factor (TF) phase are used to make the transitions of the agents from exploration to exploitation gradually concerning the amount of time required. The mBWO undergoes comparison to the original BWO and 10 additional optimizers using 29 CEC2017 functions. Eight engineering ...