Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapi...
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Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking. However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed. The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows. The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed. The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability. Thus, the CSA can be applied to optimization problems very easily. This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm. The two main operators are the exchange and local search operators. It also implements a local search operator to enhance the quality of the best solutions from the two systems. Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms. (c) 2019 The Authors. Published by Atlantis Press SARL.
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 unconstraine...
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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. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
This paper proposes a new algorithm for solving systems of complex nonlinear equations as an optimization problem. A hybridization algorithm from two inspired algorithms, grey wolf optimizer (GWO) and differential evo...
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This paper proposes a new algorithm for solving systems of complex nonlinear equations as an optimization problem. A hybridization algorithm from two inspired algorithms, grey wolf optimizer (GWO) and differential evolution (DE) is named GWO-DE. A new improving encircling prey and new crossover technique is used for updating the new agents of GWO-DE based on the generated agents of DE and GWO. Since GWO-DE has the advantages over GWO and DE, it subdues the inability of GWO and DE for solving unconstrained optimization problems and systems of nonlinear equations. Numerical experiments of 13 unconstrained optimization problems in 100 dimension and seven benchmark systems of nonlinear equations are employed to test the performance of GWO-DE. The non-parametric Wilcoxon statistical test and Friedman test are conducted for this study. Empirical results show that GWO-DE is able to circumvent other algorithms in the literature by getting the optimum solutions for most of systems of nonlinear equations and optimization problems and demonstrates its efficiency in comparison with other existing algorithms.
In today's deregulated energy market, improving grid management in generating power with load optimization is a critical challenge. It is also perilous that the system does not have any problems owing to transmiss...
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In today's deregulated energy market, improving grid management in generating power with load optimization is a critical challenge. It is also perilous that the system does not have any problems owing to transmission line clogs. The Butterfly Optimization algorithm is used for load balancing and load optimization in electricity markets. The proposed approach integrates Particle Swarm Optimization and Grey Wolf Optimizer, merging them with Butterfly Optimization algorithm as a hybridised form to enhance exploration and exploitation skills. The benefit of the Butterfly Optimization algorithm in general, as well as when it is employed to address difficult optimization issues, is validated using the New England 39 bus test system. The amalgamated algorithm approach was compared to other established meta-heuristic algorithms for the reactive power management under variable loading conditions. Using the realistic New England 39 bus system, the suggested algorithm minimizes transmission losses by 6.344% and operating costs by 6.347% with respect to the base case, respectively. The research work reveals that proposed amalgamated algorithm employing Butterfly Optimization algorithm, Grey Wolf Optimizer, and Particle Swarm Optimization performs better and offers more potential in a range of situations. The proposed technique mathematical validation indicated that it has the capacity to tackle complex optimization issues and compete with contemporary peer-reviewed literature solutions.
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