A New Linear Optimization Technique Coupling Evolutionary Algorithm for Solving Multiobjective Optimization Problems

2009 ◽  
Author(s):  
Kezong Tang ◽  
Jingyu Yang ◽  
Shang Gao
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Optimization Technique ◽  
Multiobjective Optimization Problems

A Dimensional Diversity Based Hybrid Multiobjective Evolutionary Algorithm for Optimization Problem

2016 ◽  
Vol 30 (07) ◽  
pp. 1659020 ◽  
Author(s):  
Peng Wang ◽  
Changsheng Zhang ◽  
Bin Zhang ◽  
Tingting Liu ◽  
Jiaxuan Wu
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Experimental Results ◽  
Selection Scheme ◽  
Genetic Operator ◽  
Hybrid Evolutionary Algorithm ◽  
Multiobjective Optimization Problems

Multiobjective density driven evolutionary algorithm (MODdEA) has been quite successful in solving multiobjective optimization problems (MOPs). To further improve its performance and address its deficiencies, this paper proposes a hybrid evolutionary algorithm based on dimensional diversity (DD) and firework explosion (FE). DD is defined to reflect the diversity degree of population dimension. Based on DD, a selection scheme is designed to balance diversity and convergence. A hybrid variation based on FE and genetic operator is designed to facilitate diversity of population. The proposed algorithm is tested on 14 tests problems with diverse characteristics and compared with three state-of-the-art designs. Experimental results show that the proposed design is better or at par with the chosen state-of-the-art algorithms for multiobjective optimization.

scholarly journals A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Cai Dai ◽  
Yuping Wang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Multiobjective Evolutionary Algorithm ◽  
Multiobjective Optimization Problems ◽  
Objective Space

In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms.

scholarly journals Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Yanyan Tan ◽  
Xue Lu ◽  
Yan Liu ◽  
Qiang Wang ◽  
Huaxiang Zhang
Keyword(s):  
Multiobjective Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Optimization Method ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Multiobjective Optimization Problems ◽  
Distinct Features

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

Sign in / Sign up

Export Citation Format

Share Document