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Solving Nonlinear Systems and Unconstrained Optimization Problems by Hybridizing Whale Optimization Algorithm and Flower Pollination Algorithm - 2021

solving-nonlinear-systems-and-unconstrained-optimization-problems-by-hybridizing-whale-optimization-algorithm-and-flower-pollination-algorithm.jpg

Solving Nonlinear and Unconstrained Optimization Problems by Flower Pollination Algorithm | S - Logix

Research Area:  Metaheuristic Computing

Abstract:

This paper suggests a new hybrid algorithm by integrating two population-based algorithms: Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA), to solve complex nonlinear systems and unconstrained optimization problems. WOFPA denotes the suggested algorithm, a hybrid Whale Optimization Algorithm and Flower Pollination Algorithm. Nonlinear systems can be cast into unconstrained optimization problems, called merit functions, where the optimal solutions for the merit functions are equivalent to the solutions of nonlinear systems. WOFPA aims to decrease the execution time and the complexity of WOA and FPA. WOFPA has the advantages of WOA and FPA; WOFPA is a high-quality algorithm to solve both problems, nonlinear systems and unconstrained optimization problems. For example, FPA may have a premature convergence in the local optima, and WOFPA subdues the disadvantage of FPA. Numerical experiments of 14 benchmarks nonlinear systems and 30 CEC 2014 benchmarks unconstrained optimization functions with various dimensions are employed to test the performance of WOFPA. To have a further investigation for the performance of WOFPA, WOFPA is compared with WOA, FPA, and other existing algorithms from the literature. Two non-parametric statistical tests, Wilcoxon statistical test and the Friedman test, are conducted for this study to check the performance of the proposed algorithms and other compared algorithms and the significance of our results. The experiment results demonstrate that WOFPA performs better than other algorithms in the literature by getting the optimum solutions for most nonlinear systems and optimization problems and proves its efficiency compared with other existing algorithms.

Keywords:  
population-based algorithms
Whale Optimization Algorithm
Flower Pollination Algorithm
high-quality
efficiency

Author(s) Name:  M.A. Tawhid, A.M. Ibrahim

Journal name:  Mathematics and Computers in Simulation

Conferrence name:  

Publisher name:  Elsevier

DOI:  https://doi.org/10.1016/j.matcom.2021.07.010

Volume Information:  Volume 190