Performance comparison of Differential Evolution, Genetic Algorithm and Particle Swarm Optimization in impedance matching of aperture coupled microstrip antennas

2011 ◽  
Author(s):  
Arindam Deb ◽  
Bhaskar Gupta ◽  
Jibendu Sekhar Roy
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Impedance Matching ◽  
Particle Swarm ◽  
Microstrip Antennas ◽  
Performance Comparison ◽  
Swarm Optimization

scholarly journals Performance Comparison between Particle Swarm Optimization and Differential Evolution Algorithms for Postman Delivery Routing Problem

2021 ◽  
Vol 11 (6) ◽  
pp. 2703
Author(s):  
Warisa Wisittipanich ◽  
Khamphe Phoungthong ◽  
Chanin Srisuwannapa ◽  
Adirek Baisukhan ◽  
Nuttachat Wisittipanit
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Particle Swarm ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Current Practices

Generally, transportation costs account for approximately half of the total operation expenses of a logistics firm. Therefore, any effort to optimize the planning of vehicle routing would be substantially beneficial to the company. This study focuses on a postman delivery routing problem of the Chiang Rai post office, located in the Chiang Rai province of Thailand. In this study, two metaheuristic methods—particle swarm optimization (PSO) and differential evolution (DE)—were applied with particular solution representation to find delivery routings with minimum travel distances. The performances of PSO and DE were compared along with those from current practices. The results showed that PSO and DE clearly outperformed the actual routing of the current practices in all the operational days examined. Moreover, DE performances were notably superior to those of PSO.

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

2021 ◽  
pp. 1-34
Author(s):  
Shailendra Aote ◽  
Mukesh M. Raghuwanshi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mathematical Optimization ◽  
Evolutionary Computing ◽  
Swarm Optimization

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

scholarly journals Hybrid method for achieving Pareto front on economic emission dispatch

2020 ◽  
Vol 10 (4) ◽  
pp. 3358
Author(s):  
Kummari Rajesh ◽  
N. Visali
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Method ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Swarm Optimization ◽  
Metaheuristic Methods

In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.

Hybridization of Biogeography-Based

2013 ◽  
Vol 2 (3) ◽  
pp. 86-101 ◽  
Author(s):  
Provas Kumar Roy ◽  
Dharmadas Mandal
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimal Power Flow ◽  
Particle Swarm ◽  
Population Based ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Swarm Optimization

The aim of this paper is to evaluate a hybrid biogeography-based optimization approach based on the hybridization of biogeography-based optimization with differential evolution to solve the optimal power flow problem. The proposed method combines the exploration of differential evolution with the exploitation of biogeography-based optimization effectively to generate the promising candidate solutions. Simulation experiments are carried on standard 26-bus and IEEE 30-bus systems to illustrate the efficacy of the proposed approach. Results demonstrated that the proposed approach converged to promising solutions in terms of quality and convergence rate when compared with the original biogeography-based optimization and other population based optimization techniques like simple genetic algorithm, mixed integer genetic algorithm, particle swarm optimization and craziness based particle swarm optimization.

Conflict Resolution Problem Solving with Bio-Inspired Metaheuristics

2016 ◽  
pp. 168-182
Author(s):  
P. B. de Moura Oliveira ◽  
E. J. Solteiro Pires
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Conflict Resolution ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Integrated Approach ◽  
Population Based ◽  
Swarm Optimization ◽  
Diverse Application

This chapter addresses nature and bio-inspired metaheuristics in the context of conflict detection and resolution problems. An approach is presented for a generalization of a population-based bio-inspired search and optimization algorithm, which is depicted for three of the most well-known and firmly established methods: the genetic algorithm, the particle swarm optimization algorithm and the differential evolution algorithm. This integrated approach to a basic general population-based bio-inspired algorithm is presented for single-objective optimization, multi-objective optimization and many-objective optimization. A revision of these three main bio-inspired algorithms is presented for conflict resolution problems in diverse application areas. A bridge between feedback controller design, genetic algorithm, particle swarm optimization and differential evolution is established using a conflict resolution approach. Finally, some perspectives concerning future trends of more recent bio-inspired meta-heuristics is presented.

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