An Improved Adaptive Approach for Elitist Nondominated Sorting Genetic Algorithm for Many-Objective Optimization
نویسندگان
چکیده
NSGA-II and its contemporary EMO algorithms were found to be vulnerable in solving many-objective optimization problems having four or more objectives. It is not surprising that EMO researchers have been concentrating in developing efficient algorithms for manyobjective optimization problems. Recently, authors suggested an extension of NSGA-II (NSGA-III) which is based on the supply of a set of reference points and demonstrated its working on three to 15-objective optimization problems. In this paper, NSGA-III’s reference point allocation task is made adaptive so that a better distribution of points can be found. The approach is compared with NSGA-III and a previous adaptive approach on a number of constrained and unconstrained many-objective optimization problems. NSGA-III and its adaptive extension proposed here open up new directions for research and development in the area of solving many-objective optimization problems
منابع مشابه
Optimal Placement and Sizing of Distributed Generation Via an Improved Nondominated Sorting Genetic Algorithm II
The use of distributed generation units in distribution networks has attracted the attention of network managers due to its great benefits. In this research, the location and determination of the capacity of distributed generation (DG) units for different purposes has been studied simultaneously. The multi-objective functions in the optimization model are reducing system line losses; reducing v...
متن کاملMulti-objective Genetic Optimization of Ethane Thermal Cracking Reactor
An industrial ethane thermal cracking reactor was modeled assuming a molecular mechanism for the reaction kinetics coupled with material, energy, and momentum balances of the reactant-product flow along the reactor. To carry out the multi-objective optimization for two objectives such as conversion and ethylene selectivity, the elitist non-dominated sorting genetic algorithm was used. The Paret...
متن کاملA fast and elitist multiobjective genetic algorithm: NSGA-II
Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( ) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based multiobjective EA (MOEA), called nondominate...
متن کاملEMCSO: An Elitist Multi-Objective Cat Swarm Optimization
This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optim...
متن کاملMechanical Component Design for Multiple Objectives Using Elitist Non-dominated Sorting GA
In this paper, we apply an elitist multi-objective genetic algorithm for solving mechanical component design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods in that they can find multiple Pareto-optimal solutions in one single simulation run. The proposed algorithm (we call NSGA-II) ...
متن کامل