The Black Widow optimization (BWO) algorithm has garnered significant attention within the realm of metaheuristic algorithms due to its potential to address diverse problems across various domains. However, a notewort...
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The Black Widow optimization (BWO) algorithm has garnered significant attention within the realm of metaheuristic algorithms due to its potential to address diverse problems across various domains. However, a noteworthy weakness of BWO is its utilization of a random selection technique, which can lead to reduced diversity, expedited convergence, and potential entrapment in local optima. This research introduces a novel approach to augment the BWO algorithm by integrating alternative selection schemes, thereby surpassing the limitations of the current selection methodology. To assess the effectiveness of these proposed variants, we employ the CEC 2019 benchmarkfunctions as the standard evaluation metric. Subsequently, we utilize the best-performing BWO version, PIBWO, to address cloud scheduling challenges. In a series of comparative experiments, PIBWO demonstrates superior performance compared to existing algorithms, showcasing remarkable enhancements in makespan reduction, energy consumption minimization, and cost efficiency. These findings underscore PIBWO's potential as a robust solution for addressing cloud task scheduling challenges, offering promising avenues for developing more sustainable and cost-effective cloud computing systems.
optimization refers to finding the optimal solution to minimize or maximize the objective function. In the field of engineering, this plays an important role in designing parameters and reducing manufacturing costs. M...
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optimization refers to finding the optimal solution to minimize or maximize the objective function. In the field of engineering, this plays an important role in designing parameters and reducing manufacturing costs. Meta-heuristics such as the grey wolf optimizer (GWO) are efficient ways to solve optimization problems. However, the GWO suffers from premature convergence or low accuracy. In this study, a team learning-based grey wolf optimizer (TLGWO), which consists of two strategies, is proposed to overcome these shortcomings. The neighbor learning strategy introduces the influence of neighbors to improve the local search ability, whereas the random learning strategy provides new search directions to enhance global exploration. Four engineering problems with constraints and 21 benchmarkfunctions were employed to verify the competitiveness of the TLGWO. The test results were compared with three derivatives of the GWO and nine other state-of-the-art algorithms. Furthermore, the experimental results were analyzed using the Friedman and mean absolute error statistical tests. The results show that the proposed TLGWO can provide superior solutions to the compared algorithms on most optimization tasks and solve engineering problems with constraints.
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