Comparison and Evaluation of Multiple Objective Genetic Algorithms for the Antenna Placement Problem

Abstract Engineering decision making involving multiple competing objectives relies on choosing a design solution from an optimal set of solutions. This optimal set of solutions, referred to as the Pareto set, represents the tradeoffs that exist between the competing objectives for different design solutions. Generation of this Pareto set is the main focus of multiple objective optimization. There are many methods to solve this type of problem. Some of these methods generate solutions that cannot be applied to problems with a combination of discrete and continuous variables. Often such solutions are obtained by an optimization technique that can only guarantee local Pareto solutions or is applied to convex problems. The main focus of this paper is to demonstrate two methods of using genetic algorithms to overcome these problems. The first method uses a genetic algorithm with some external modifications to handle multiple objective optimization, while the second method operates within the genetic algorithm with some significant internal modifications. The fact that the first method operates with the genetic algorithm and the second method within the genetic algorithm is the main difference between these two techniques. Each method has its strengths and weaknesses, and it is the objective of this paper to compare and contrast the two methods quantitatively as well as qualitatively. Two multiobjective design optimization examples are used for the purpose of this comparison.

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Parallel Single and Multiple Objectives Genetic Algorithms

Modeling Applications and Theoretical Innovations in Interdisciplinary Evolutionary Computation ◽

10.4018/978-1-4666-3628-6.ch006 ◽

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.

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Ordered Incremental Multi-Objective Problem Solving Based on Genetic Algorithms

Principal Concepts in Applied Evolutionary Computation ◽

10.4018/978-1-4666-1749-0.ch005 ◽

2012 ◽

pp. 72-98

Author(s):

Wenting Mo ◽

Sheng-Uei Guan ◽

Sadasivan Puthusserypady

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Problem Solving ◽

Benchmark Problems ◽

Multiple Objective ◽

Nsga Ii ◽

Incremental Approach ◽

Strength Pareto Evolutionary Algorithm ◽

Conflicting Objectives ◽

Open Question

Many Multiple Objective Genetic Algorithms (MOGAs) have been designed to solve problems with multiple conflicting objectives. Incremental approach can be used to enhance the performance of various MOGAs, which was developed to evolve each objective incrementally. For example, by applying the incremental approach to normal MOGA, the obtained Incremental Multiple Objective Genetic Algorithm (IMOGA) outperforms state-of-the-art MOGAs, including Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Strength Pareto Evolutionary Algorithm (SPEA) and Pareto Archived Evolution Strategy (PAES). However, there is still an open question: how to decide the order of the objectives handled by incremental algorithms? Due to their incremental nature, it is found that the ordering of objectives would influence the performance of these algorithms. In this paper, the ordering issue is investigated based on IMOGA, resulting in a novel objective ordering approach. The experimental results on benchmark problems showed that the proposed approach can help IMOGA reach its potential best performance.

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