Adaptive Balance Optimizer: A New Adaptive Metaheuristic and its Application in Solving Optimization Problem in Finance
نویسندگان
چکیده
Adaptability becomes important in developing metaheuristic algorithms, especially tackling stagnation. Unfortunately, almost all metaheuristics are not equipped with an adaptive approach that makes them change their strategy when stagnation happens during iteration. Based on this consideration, a new metaheuristic, called balance optimizer (ABO), is proposed paper. ABO's unique focuses exploitation improvement and switching to exploration ABO also uses balanced between by performing two sequential searches, whatever circumstance it faces. These searches consist of one guided search random search. Moreover, deploys both strict acceptance non-strict approach. In work, challenged solve set 23 classic functions as theoretical optimization problem portfolio the use case for practical problem. optimization, should optimize quantity ten stocks energy mining sector listed IDX30 index. evaluation, competed five other metaheuristics: marine predator algorithm (MPA), golden (GSO), slime mold (SMA), northern goshawk (NGO), zebra (ZOA). The simulation result shows better than MPA, GSO, SMA, NGO, ZOA solving 21, 18, 16, 11, 8, respectively, functions. Meanwhile, third-best performer
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ژورنال
عنوان ژورنال: International Journal of Advanced Computer Science and Applications
سال: 2023
ISSN: ['2158-107X', '2156-5570']
DOI: https://doi.org/10.14569/ijacsa.2023.0140415