Tchebycheff Method-based Evolutionary Algorithm for Multiobjective Optimization
نویسندگان
چکیده
RAO, SUNIL, MURALI. Tchebycheff Method-based Evolutionary Algorithm for Multiobjective Optimization (Under the direction of Dr. Ranji Ranjithan) In the operations research literature, the Tchebycheff method has been demonstrated to be a useful approach for exploring the non-dominated solutions for multiobjective optimization problems. While this method has been investigated with mathematical programming-based solution approaches, its application with modern heuristic search procedures is lacking. As heuristic search procedures continue to show promise as practical solution approaches for realistic engineering problems typically with multiple design objectives, the need for their applications in multiobjective optimization is becoming increasingly important. This paper investigates a new evolutionary algorithm-based multiobjective optimization procedure that builds upon the Tchebycheff method. By embedding a beneficial seeding approach, the efficiency of the algorithm is expectedly enhanced. This Tchebycheff Method-based Evolutionary Algorithm (TMEA) is tested and evaluated using a suite of 2-objective test problems, representing a range of complexities in the decision space as well as in the objective space. The performance of TMEA with those of other multiobjective evolutionary algorithms are compared using several performance metrics that are reported in the literature. For the problems considered in this paper, TMEA performs relatively well in generating non-dominated solutions that are close to the known Pareto set and are well distributed in the non-inferior space. TCHEBYCHEFF METHOD-BASED EVOLUTIONARY ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION
منابع مشابه
Multiobjective Imperialist Competitive Evolutionary Algorithm for Solving Nonlinear Constrained Programming Problems
Nonlinear constrained programing problem (NCPP) has been arisen in diverse range of sciences such as portfolio, economic management etc.. In this paper, a multiobjective imperialist competitive evolutionary algorithm for solving NCPP is proposed. Firstly, we transform the NCPP into a biobjective optimization problem. Secondly, in order to improve the diversity of evolution country swarm, and he...
متن کاملMOTGA: A multiobjective Tchebycheff based genetic algorithm for the multidimensional knapsack problem
This paper presents a newmultiobjective genetic algorithm based on the Tchebycheff scalarizing function, which aims to generate a good approximation of the nondominated solution set of the multiobjective problem. The algorithm performs several stages, each one intended for searching potentially nondominated solutions in a different part of the Pareto front. Pre-defined weight vectors act as piv...
متن کاملA multiobjective evolutionary algorithm based on decomposition with normal boundary intersection for traffic grooming in optical networks
This paper presents a multiobjective optimization algorithm for solving tri-objective optimization problems with objectives of very different scales. As a version of the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), it adopts the Normal Boundary Intersection (NBI) based Tchebycheff approach to decompose a multiobjective optimization problem into a number of single objec...
متن کاملMultiobjective Optimization of a Quadruped Robot Locomotion using a Genetic Algorithm
In this work, it is described a gait multiobjective optimization system that allows to obtain fast but stable robot quadruped crawl gaits. We combine bioinspired Central Patterns Generators (CPGs) and Genetic Algorithms (GA). A motion architecture based on CPGs oscillators is used to model the locomotion of the robot dog and a GA is used to search parameterizations of the CPGs parameters which ...
متن کاملPareto-based Cost Simulated Annealing for Multiobjective Optimization
In this paper, a multiobjective simulated annealing (MOSA) method is introduced and discussed with the multiobjective evolutionary algorithms (MOEAs). Though the simulated annealing is a very powerful search algorithm and has shown good results in various singleobjective optimization fields, it has been seldom used for the multiobjective optimization because it conventionally uses only one sear...
متن کامل