A Multi-Objective Genetic Algorithm Based on Fitting and Interpolation
33
TL;DR: MOGA/F and MogA/I are compared with the traditional methods, non-dominated sorting genetic algorithm-II and multi-objective evolutionary algorithm based on decomposition, by optimizing the mathematical problems and show that MOG a/F has a much higher performance in terms of diversity and convergence of the final solutions.
read more
Abstract: Considering the diversity of uniform distribution for the solutions of multi-objective optimization problems, we propose the multi-objective genetic algorithm based on fitting (MOGA/F) and interpolation (MOGA/I). The selected operator is based on the optimal reference points uniformly distributed in the objective space, which is calculated by applying a fitting function or interpolation method from a finite set of objective values. After sorting the ranks of the population, the objective space for the last front can be easily calculated by using fitting and interpolation functions, and the uniformly distributed points can be obtained without parameter setting. The individuals with the shortest Euclidean distance to the reference points are chosen according to the error matrix. This method can maintain the diversity and spread of the solutions without destroying the convergence. In this paper, MOGA/F and MOGA/I are compared with the traditional methods, non-dominated sorting genetic algorithm-II and multi-objective evolutionary algorithm based on decomposition, by optimizing the mathematical problems. The numerical examples show that MOGA/F and MOGA/I have a much higher performance in terms of diversity and convergence of the final solutions.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
An Enhanced Fast Non-Dominated Solution Sorting Genetic Algorithm for Multi-objective Problems
TL;DR: Wang et al. as mentioned in this paper proposed an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, which can improve PS distribution and convergence and maintain PF precision.
327
An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems
01 Mar 2022
TL;DR: Wang et al. as discussed by the authors proposed an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, which can improve PS distribution and convergence and maintain PF precision.
186
Adaptive Multiobjective Particle Swarm Optimization Based on Evolutionary State Estimation
TL;DR: A novel adaptive multiobjective particle swarm optimization (MOPSO) is proposed on the basis of an evolutionary state estimation mechanism, which is used to detect the evolutionary environment whether in exploitation or exploration state.
53
A New Evolutionary Multiobjective Model for Traveling Salesman Problem
TL;DR: An improved method for GAs based on a novel evolutionary computational model, named the Physarum-inspired computational model (PCM), which is based on the prior knowledge of the PCM and optimized to enhance the distribution of solutions.
Multi-objective design approach of passive filters for single-phase distributed energy grid integration systems using particle swarm optimization
Mohamed Azab,Mohamed Azab +1 more
TL;DR: The main contribution of this paper is the utilization of evolutionary optimization technique to achieve an optimum design of passive grid filters that can optimize simultaneously several contradictory goals such as achieving the maximum possible harmonic attenuation at the lowest possible filter size.
34
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Qingfu Zhang,Hui Li +1 more
TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints
Kalyanmoy Deb,Himanshu Jain +1 more
TL;DR: A reference-point-based many-objective evolutionary algorithm that emphasizes population members that are nondominated, yet close to a set of supplied reference points is suggested that is found to produce satisfactory results on all problems considered in this paper.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
Comparison of multiobjective evolutionary algorithms: empirical results
Eckart Zitzler,Kalyanmoy Deb,Lothar Thiele +2 more
- 01 Jan 1999
TL;DR: In this article, the authors provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions, each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front.
3.9K