Multiple Objective Optimization with Weighted Superposition Attraction–Repulsion (moWSAR) Algorithm
Adil Baykasoğlu
Chapter 8 in Modeling and Advanced Techniques in Modern Economics, 2022, pp 173-185 from World Scientific Publishing Co. Pte. Ltd.
Abstract:
Weighted Superposition Attraction–Repulsion (WSAR) algorithm is a recently proposed swarm intelligence-based metaheuristic algorithm that is inspired from superposition principle of physics and attracted movements of agents. WSAR has been applied to many single objective unconstrained and constrained complex optimization problems successfully. In the present study, WSAR is applied to Multiple Objective Optimization (MOO) problems for the first time in the literature. Details of the WSAR algorithm along with applications to MOO problems that are collected from the literature are presented in this study. It is shown that WSAR is competitive and able to generate Pareto optimal solutions.
Keywords: Harmonic Regression; Periodograms; Consumer Price Index; Food Inflation; Turkey; Gaussian Distribution; Europe Union; GDP; Panel Data; Spatial Regression; Measurement Errors; Nonlinear Time Series; Chaotic Time Series; Weibull Distribution; Location Parameters; Fiducial Approach; Hypothesis Testing; Green Swan; Financial Stability; Annex II Countries; Financial Time Series; Kernels; Stock Index; Machine Learning; Statistical Learning; Optimization; WSAR Algorithm; Deep Neural Networks; Phyton; Parameter Estimation; COVID-19; Clustering Analyses; Artificial Neural Networks; Performance Criteria; Time Series Forecasting; Statistical Inference (search for similar items in EconPapers)
JEL-codes: C1 C4 C5 C6 C63 (search for similar items in EconPapers)
Date: 2022
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