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• W4386205082 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." @default.
• W4386205082 created "2023-08-28" @default.
• W4386205082 creator A5001043147 @default.
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• W4386205082 date "2023-01-01" @default.
• W4386205082 modified "2023-09-25" @default.
• W4386205082 title "A Simple Hybrid Local Search Algorithm for Solving Optimization Problems" @default.
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• W4386205082 doi "https://doi.org/10.1007/978-3-031-39811-7_20" @default.
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