The Experts below are selected from a list of 218958 Experts worldwide ranked by ideXlab platform
Manojkumar Ramteke - One of the best experts on this subject based on the ideXlab platform.
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A simplified multi-objective Particle Swarm optimization algorithm
Swarm Intelligence, 2019Co-Authors: Vibhu Trivedi, Pushkar Varshney, Manojkumar RamtekeAbstract:Particle Swarm optimization is a popular nature-inspired metaheuristic algorithm and has been used extensively to solve single- and multi-objective optimization problems over the last two decades. Several local and global search strategies, and learning and parameter adaptation strategies have been included in Particle Swarm optimization to improve its performance over the years. Most of these approaches are observed to increase the number of user-defined parameters and algorithmic steps resulting in an increased complexity of the algorithm. This paper presents a simplified multi-objective Particle Swarm optimization algorithm in which the exploitation (guided) and exploration (random) moves are simplified using a detailed qualitative analysis of similar existing operators present in the real-coded elitist non-dominated sorting genetic algorithm and the Particle Swarm optimization algorithm. The developed algorithm is then tested quantitatively on 30 well-known benchmark problems and compared with a real-coded elitist non-dominated sorting genetic algorithm, and its variant with a simulated binary jumping gene operator and multi-objective non-dominated sorting Particle Swarm optimization algorithm. In the comparison, the developed algorithm is found to be superior in terms of convergence speed. It is also found to be better with respect to four recent multi-objective Particle Swarm optimization algorithms and four differential evolution variants in an extended comparative analysis. Finally, it is applied to a newly formulated industrial multi-objective optimization problem of a residue (bottom product from the crude distillation unit) fluid catalytic cracking unit where it shows a better performance than the other compared algorithms.
Vibhu Trivedi - One of the best experts on this subject based on the ideXlab platform.
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A simplified multi-objective Particle Swarm optimization algorithm
Swarm Intelligence, 2019Co-Authors: Vibhu Trivedi, Pushkar Varshney, Manojkumar RamtekeAbstract:Particle Swarm optimization is a popular nature-inspired metaheuristic algorithm and has been used extensively to solve single- and multi-objective optimization problems over the last two decades. Several local and global search strategies, and learning and parameter adaptation strategies have been included in Particle Swarm optimization to improve its performance over the years. Most of these approaches are observed to increase the number of user-defined parameters and algorithmic steps resulting in an increased complexity of the algorithm. This paper presents a simplified multi-objective Particle Swarm optimization algorithm in which the exploitation (guided) and exploration (random) moves are simplified using a detailed qualitative analysis of similar existing operators present in the real-coded elitist non-dominated sorting genetic algorithm and the Particle Swarm optimization algorithm. The developed algorithm is then tested quantitatively on 30 well-known benchmark problems and compared with a real-coded elitist non-dominated sorting genetic algorithm, and its variant with a simulated binary jumping gene operator and multi-objective non-dominated sorting Particle Swarm optimization algorithm. In the comparison, the developed algorithm is found to be superior in terms of convergence speed. It is also found to be better with respect to four recent multi-objective Particle Swarm optimization algorithms and four differential evolution variants in an extended comparative analysis. Finally, it is applied to a newly formulated industrial multi-objective optimization problem of a residue (bottom product from the crude distillation unit) fluid catalytic cracking unit where it shows a better performance than the other compared algorithms.
Ying Liu - One of the best experts on this subject based on the ideXlab platform.
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Randomization in Particle Swarm optimization for global search ability
Expert Systems with Applications, 2011Co-Authors: Dawei Zhou, Xiang Gao, Guohai Liu, Congli Mei, Dong Jiang, Ying LiuAbstract:This paper introduces a novel Particle Swarm optimization (PSO) with random position to improve the global search ability of Particle Swarm optimization with linearly decreasing inertia weight (IWPSO). Standard Particle Swarm optimization and most of its derivations are easy to fall into local optimum of the problem by lacking of mutation in those operations. Inspired by the acceptance probability in simulated annealing algorithm, the random factors could be put in Particle Swarm optimization appropriately. Consequently, the concept of the mutation is introduced to the algorithm, and the global search ability would be improved. A Particle Swarm optimization with random position (RPPSO) is tested using seven benchmark functions with different dimensions and compared with four well-known derivations of Particle Swarm optimization. Experimental results show that the proposed Particle Swarm optimization could keep the diversity of Particles, and have better global search performance.
Pushkar Varshney - One of the best experts on this subject based on the ideXlab platform.
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A simplified multi-objective Particle Swarm optimization algorithm
Swarm Intelligence, 2019Co-Authors: Vibhu Trivedi, Pushkar Varshney, Manojkumar RamtekeAbstract:Particle Swarm optimization is a popular nature-inspired metaheuristic algorithm and has been used extensively to solve single- and multi-objective optimization problems over the last two decades. Several local and global search strategies, and learning and parameter adaptation strategies have been included in Particle Swarm optimization to improve its performance over the years. Most of these approaches are observed to increase the number of user-defined parameters and algorithmic steps resulting in an increased complexity of the algorithm. This paper presents a simplified multi-objective Particle Swarm optimization algorithm in which the exploitation (guided) and exploration (random) moves are simplified using a detailed qualitative analysis of similar existing operators present in the real-coded elitist non-dominated sorting genetic algorithm and the Particle Swarm optimization algorithm. The developed algorithm is then tested quantitatively on 30 well-known benchmark problems and compared with a real-coded elitist non-dominated sorting genetic algorithm, and its variant with a simulated binary jumping gene operator and multi-objective non-dominated sorting Particle Swarm optimization algorithm. In the comparison, the developed algorithm is found to be superior in terms of convergence speed. It is also found to be better with respect to four recent multi-objective Particle Swarm optimization algorithms and four differential evolution variants in an extended comparative analysis. Finally, it is applied to a newly formulated industrial multi-objective optimization problem of a residue (bottom product from the crude distillation unit) fluid catalytic cracking unit where it shows a better performance than the other compared algorithms.
Pedram Ghamisi - One of the best experts on this subject based on the ideXlab platform.
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Fractional Order Darwinian Particle Swarm Optimization - Fractional Order Darwinian Particle Swarm Optimization
SpringerBriefs in Applied Sciences and Technology, 2016Co-Authors: Micael S. Couceiro, Pedram GhamisiAbstract:This book examines the bottom-up applicability of Swarm intelligence to solving multiple problems, such as curve fitting, image segmentation, and Swarm robotics. It compares the capabilities of some of the better-known bio-inspired optimization approaches, especially Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO) and the recently proposed Fractional Order Darwinian Particle Swarm Optimization (FODPSO), and comprehensively discusses their advantages and disadvantages. Further, it demonstrates the superiority and key advantages of using the FODPSO algorithm, su
