Multi-Objective Optimization Based on Brain Storm Optimization Algorithm

2013 ◽  
Vol 4 (3) ◽  
pp. 1-21 ◽  
Author(s):  
Yuhui Shi ◽  
Jingqian Xue ◽  
Yali Wu
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Solution Space ◽  
Population Based ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Brain Storm Optimization ◽  
Objective Space ◽  
Different Characteristics ◽  
Single Objective

In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that the proposed multi-objective brain storm optimization algorithm is a very promising algorithm, at least for solving these tested multi-objective optimization problems.

A CLUSTERING-BASED NICHING FRAMEWORK FOR THE APPROXIMATION OF EQUIVALENT PARETO-SUBSETS

2011 ◽  
Vol 10 (03) ◽  
pp. 295-311 ◽  
Author(s):  
OLIVER KRAMER ◽  
HOLGER DANIELSIEK
Keyword(s):  
Optimization Problems ◽  
Kernel Density ◽  
Population Based ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
Pareto Sets ◽  
Density Clustering ◽  
Different Characteristics ◽  
Different Parts

In many optimization problems in practice, multiple objectives have to be optimized at the same time. Some multi-objective problems are characterized by multiple connected Pareto-sets at different parts in decision space — also called equivalent Pareto-subsets. We assume that the practitioner wants to approximate all Pareto-subsets to be able to choose among various solutions with different characteristics. In this work, we propose a clustering-based niching framework for multi-objective population-based approaches that allows to approximate equivalent Pareto-subsets. Iteratively, the clustering process assigns the population to niches, and the multi-objective optimization process concentrates on each niche independently. Two exemplary hybridizations, rake selection and DBSCAN, as well as SMS-EMOA and kernel density clustering demonstrate that the niching framework allows enough diversity to detect and approximate equivalent Pareto-subsets.

A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

2017 ◽  
pp. 491-521 ◽  
Author(s):  
Rizk M. Rizk-Allah ◽  
Aboul Ella Hassanien
Keyword(s):  
Optimization Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Fruit Fly Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

An Interactive Neural Network for Constrained Multi-Objective Optimization with Application to the Design of Digital Filters

2012 ◽  
Vol 433-440 ◽  
pp. 2808-2816
Author(s):  
Jian Jin Zheng ◽  
You Shen Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Complexity ◽  
Digital Filters ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

This paper presents a new interactive neural network for solving constrained multi-objective optimization problems. The constrained multi-objective optimization problem is reformulated into two constrained single objective optimization problems and two neural networks are designed to obtain the optimal weight and the optimal solution of the two optimization problems respectively. The proposed algorithm has a low computational complexity and is easy to be implemented. Moreover, the proposed algorithm is well applied to the design of digital filters. Computed results illustrate the good performance of the proposed algorithm.

A Kind of Evolutionary Multi-Objective Optimization Algorithm Based on AIS

2013 ◽  
Vol 442 ◽  
pp. 419-423
Author(s):  
Ming Song Li
Keyword(s):  
Immune System ◽  
Optimization Algorithm ◽  
Clonal Selection ◽  
Research Area ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Objective Space ◽  
Speed Up ◽  
Important Research Area ◽  
Intelligent Information

Problem of multi-objective optimization based on Artificial Immune System (AIS) is an important research area of current evolutionary computing. Starting from the intelligent information processing mechanism of immune theory and the immune system itself, a kind of evolutionary multi-objective optimization algorithm based on AIS is proposed. Clonal selection, scattered crossover and hypermutation based on the learning mechanism are characteristics of the algorithm. Algorithm implements clonal selection according to the distribution of individuals in the objective space, which benefit obtaining Pareto optimal boundary distributed more widely and speed up the convergence. Compared with the existing algorithms, the algorithm has been greatly improved in convergence, diversity, and distribution of solutions.

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