Parallel Multi-Criterion Genetic Algorithms

2016 ◽  
Vol 7 (1) ◽  
pp. 50-62 ◽  
Author(s):  
Bhabani Shankar Prasad Mishra ◽  
Subhashree Mishra ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Potential Application ◽  
Processing Time ◽  
Acceptable Solution ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

The objective of this paper is to study the existing and current research on parallel multi-objective genetic algorithms (PMOGAs) through an intensive experiment. Many early efforts on parallelizing multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution of them with various examples. Further, the authors tried to identify some of the issues that have not yet been studied systematically under the umbrella of parallel multi-objective genetic algorithms. Finally, some of the potential application of parallel multi objective genetic algorithm is discussed.

Parallel Multi-Criterion Genetic Algorithms

2016 ◽  
pp. 172-186
Author(s):  
Bhabani Shankar Prasad Mishra ◽  
Subhashree Mishra ◽  
Sudhansu Sekhar Singh
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Potential Application ◽  
Processing Time ◽  
Acceptable Solution ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

The objective of this paper is to study the existing and current research on parallel multi-objective genetic algorithms (PMOGAs) through an intensive experiment. Many early efforts on parallelizing multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution of them with various examples. Further, the authors tried to identify some of the issues that have not yet been studied systematically under the umbrella of parallel multi-objective genetic algorithms. Finally, some of the potential application of parallel multi objective genetic algorithm is discussed.

Parallel Single and Multiple Objectives Genetic Algorithms

2013 ◽  
pp. 73-110
Author(s):  
B. S. P. Mishra ◽  
S. Dehuri ◽  
R. Mall ◽  
A. Ghosh
Keyword(s):  
Genetic Algorithms ◽  
Processing Time ◽  
Multiple Objectives ◽  
Multiple Objective ◽  
Acceptable Solution ◽  
Multi Objective ◽  
Potential Applications

This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution. However, some parallel single and multi-objective genetic algorithms converged to better solutions as compared to comparable sequential single and multiple objective genetic algorithms. The authors review several representative models for parallelizing single and multi-objective genetic algorithms. Further, some of the issues that have not yet been studied systematically are identified in the context of parallel single and parallel multi-objective genetic algorithms. Finally, some of the potential applications of parallel multi-objective GAs are discussed.

Parallel Single and Multiple Objectives Genetic Algorithms

2011 ◽  
Vol 2 (2) ◽  
pp. 21-57 ◽  
Author(s):  
B. S. P. Mishra ◽  
S. Dehuri ◽  
R. Mall ◽  
A. Ghosh
Keyword(s):  
Genetic Algorithms ◽  
Processing Time ◽  
Multiple Objectives ◽  
Multiple Objective ◽  
Acceptable Solution ◽  
Multi Objective ◽  
Potential Applications

This paper critically reviews the reported research on parallel single and multi-objective genetic algorithms. Many early efforts on single and multi-objective genetic algorithms were introduced to reduce the processing time needed to reach an acceptable solution. However, some parallel single and multi-objective genetic algorithms converged to better solutions as compared to comparable sequential single and multiple objective genetic algorithms. The authors review several representative models for parallelizing single and multi-objective genetic algorithms. Further, some of the issues that have not yet been studied systematically are identified in the context of parallel single and parallel multi-objective genetic algorithms. Finally, some of the potential applications of parallel multi-objective GAs are discussed.

scholarly journals Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems

1999 ◽  
Vol 7 (3) ◽  
pp. 205-230 ◽  
Author(s):  
Kalyanmoy Deb
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimization Problems ◽  
Test Problems ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Single Objective

In this paper, we study the problem features that may cause a multi-objective genetic algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such features helps us develop difficult test problems for multi-objective optimization. Multi-objective test problems are constructed from single-objective optimization problems, thereby allowing known difficult features of single-objective problems (such as multi-modality, isolation, or deception) to be directly transferred to the corresponding multi-objective problem. In addition, test problems having features specific to multi-objective optimization are also constructed. More importantly, these difficult test problems will enable researchers to test their algorithms for specific aspects of multi-objective optimization.

A New Multi Objective Genetic Algorithm: Fitness Aggregated Genetic Algorithm (FAGA) for Vehicle Routing Problem

2014 ◽  
Vol 984-985 ◽  
pp. 1261-1268 ◽  
Author(s):  
V. Sivaram Kumar ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Suggested Algorithm ◽  
Multi Objective ◽  
Total Distance ◽  
Multi Objective Genetic Algorithm ◽  
Assignment Approach

This paper presents multi objective vehicle routing problem in which the total distance travelled by the vehicles and total number of vehicles used are minimized. In general, fitness assignment procedure, as one of the important operators, influences the effectiveness of multi objective genetic algorithms. In this paper genetic algorithm with different fitness assignment approach and specialized crossover called Fitness Aggregated Genetic Algorithm (FAGA) is introduced for solving the problem. The suggested algorithm is investigated on large number of popular benchmarks for vehicle routing problem. It is observed from the results that the suggested new algorithm is very effective and the solutions are competitive with the best known results.

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