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.

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.

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 Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

scholarly journals Ply stacking sequence optimization of composite driveshaft using particle swarm optimization algorithm

2014 ◽  
Vol 5 ◽  
pp. A16 ◽  
Author(s):  
K. Manjunath ◽  
T. Rangaswamy
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Epoxy Composite ◽  
Particle Swarm ◽  
Stacking Sequence ◽  
Swarm Optimization ◽  
Stacking Sequence Optimization ◽  
Single Piece

In this paper an attempt has been made to optimize ply stacking sequence of single piece E-Glass/Epoxy, HM Carbon/Epoxy and Boron/Epoxy composite drive shafts using particle swarm optimization (PSOA). PSOA programme is developed using MATLAB V 7 to optimize the ply stacking sequence with an objective of weight minimization. The weight savings of the E-Glass/Epoxy, HM Carbon/Epoxy and Boron/Epoxy shaft are 51%, 87% and 85% of the steel shaft respectively. The optimum results of PSOA obtained are compared with results of genetic algorithm (GA) and found that PSOA yields better results than GA.

scholarly journals An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaobing Yu ◽  
Jie Cao ◽  
Haiyan Shan ◽  
Li Zhu ◽  
Jun Guo
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Stochastic Search ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Local Optima

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

Sign in / Sign up

Export Citation Format

Share Document