Abstract
Optimization in engineering is an important domain of operations research that gains a lot of attention nowadays. Optimization may be constrained or unconstrained; single objective or multi-objective. With the extent of computing power available today, optimization algorithms are capable of handling several constraints and more variables. This paper proposes a new, simple hybrid local search metaheuristic algorithm for solving single objective, un-constrained problems in the optimization domain. The new algorithm is a hybrid one that generates the initial population randomly and, iteratively move towards optimal/near-optimal solutions. It uses one tuning parameter and a single random number. The performance is analyzed using ninety-three benchmark functions including the 100-digit challenge test suite of IEEE Congress on Evolutionary Computation (CEC2019). The number of dimensions varies from one to 100. The results obtained are compared with a few efficient algorithms including Sine Cosine Algorithm and Arithmetic Optimization Algorithm. The analyses show that the newly proposed hybrid local search algorithm is effective and competitive.
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Baskar, A., Anthony Xavior, M. (2023). A Simple Hybrid Local Search Algorithm for Solving Optimization Problems. In: Mercier-Laurent, E., Fernando, X., Chandrabose, A. (eds) Computer, Communication, and Signal Processing. AI, Knowledge Engineering and IoT for Smart Systems. ICCCSP 2023. IFIP Advances in Information and Communication Technology, vol 670. Springer, Cham. https://doi.org/10.1007/978-3-031-39811-7_20
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