Adaptive Balance Optimizer: A New Adaptive Metaheuristic and its Application in Solving Optimization Problem in Finance

Abstract

Adaptability becomes important in developing metaheuristic algorithms, especially in 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 an adaptive balance optimizer (ABO), is proposed in this paper. ABO's unique strategy focuses on exploitation when improvement happens and switching to exploration during stagnation. ABO also uses a balanced strategy between exploration and exploitation by performing two sequential searches, whatever circumstance it faces. These sequential searches consist of one guided search and one random search. Moreover, ABO also deploys both a strict acceptance approach and a non-strict acceptance approach. In this work, ABO is challenged to solve a set of 23 classic functions as a theoretical optimization problem and a portfolio optimization problem as the use case for the practical optimization problem. In portfolio optimization, ABO should optimize the quantity of ten stocks in the energy and mining sector listed in the IDX30 index. In this evaluation, ABO is competed with five other metaheuristics: marine predator algorithm (MPA), golden search optimizer (GSO), slime mold algorithm (SMA), northern goshawk optimizer (NGO), and zebra optimization algorithm (ZOA). The simulation result shows that ABO is better than MPA, GSO, SMA, NGO, and ZOA in solving 21, 18, 16, 11, and 8, respectively, in solving 23 functions. Meanwhile, ABO becomes the third-best performer in solving the portfolio optimization problem.