A Modification of MOEA/D for Solving Multi-Objective Optimization Problems

2018 ◽  
Vol 22 (2) ◽  
pp. 214-223
Author(s):  
Wei Zheng ◽  
Yanyan Tan ◽  
Meng Gao ◽  
Wenzhen Jia ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Design Method ◽  
Experimental Studies ◽  
Weight Vector ◽  
The Other ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Proposed Modification ◽  
Almost All ◽  
Modified Algorithm

In this paper, a novel modified algorithm based on MOEA/D, abbreviated as mMOEA/D, is proposed for well solving the multi-objective optimization problems. Our proposed mMOEA/D inherits from MOEA/D. In mMOEA/D, a novel elastic weight vectors design method is introduced and adopted to make those weight vectors spread more widely. On the other hand, a flexible and efficient trail DE operator is designed and used in mMOEA/D for further enhancing the performance of MOEA/D. Three groups of experimental studies are carried out. Proposed mMOEA/D is compared with the four state-the-art multi-objective optimization evolutionary algorithms on solving the multi-objective optimization problems with many objectives, and the other is that mMOEA/D is compared with MOEA/D-DE, an improved version of MOEA/D, on solving the multi-objective optimization problems with complicated PS shapes. The versions of mMOEA/D with the improvement of weight vector and DE operator are compared with MOEA/D-DE to solve multi-objective optimization problems at last. The experimental results show that mMOEA/D performs the best on almost all test instances. In other words, our proposed modification of MOEA/D is effective.

Population Decomposition-Based Greedy Approach Algorithm for the Multi-Objective Knapsack Problems

2017 ◽  
Vol 31 (04) ◽  
pp. 1759006 ◽  
Author(s):  
Jiawei Yuan ◽  
Hai-Lin Liu ◽  
Chaoda Peng
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Weight Vector ◽  
Knapsack Problems ◽  
Multi Objective Optimization ◽  
Greedy Approach ◽  
Greedy Strategy ◽  
Multi Objective ◽  
Repair Strategy

Despite the effectiveness of the decomposition-based multi-objective evolutional algorithm (MOEA/D-M2M) in solving continuous multi-objective optimization problems (MOPs), its performance in addressing 0/1 multi-objective knapsack problems (MOKPs) has not been fully explored. In this paper, we use MOEA/D-M2M with an improved greedy repair strategy to solve MOKPs. It first decomposes an MOKP into a number of simple optimization subproblems and solves them in a collaborative way. Each subproblem has its own subpopulation, and then an improved greedy strategy is introduced to improve the performance of the proposed algorithm on MOKPs. Therein, a weight vector chosen randomly from a corresponding subpopulation is utilized to repair infeasible individuals or improve feasible individuals to have a better fitness, which improves the convergence of the population. Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA/D in terms of finding better approximations to the Pareto front.

scholarly journals Comparison of synchronous and asynchronous parallelization of extreme surrogate-assisted multi-objective evolutionary algorithm

2020 ◽  
Author(s):  
Tomohiro Harada ◽  
Misaki Kaidan ◽  
Ruck Thawonmas
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Computing Time ◽  
The Other ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Evaluation Time ◽  
Surrogate Function

Abstract This paper investigates the integration of a surrogate-assisted multi-objective evolutionary algorithm (MOEA) and a parallel computation scheme to reduce the computing time until obtaining the optimal solutions in evolutionary algorithms (EAs). A surrogate-assisted MOEA solves multi-objective optimization problems while estimating the evaluation of solutions with a surrogate function. A surrogate function is produced by a machine learning model. This paper uses an extreme learning surrogate-assisted MOEA/D (ELMOEA/D), which utilizes one of the well-known MOEA algorithms, MOEA/D, and a machine learning technique, extreme learning machine (ELM). A parallelization of MOEA, on the other hand, evaluates solutions in parallel on multiple computing nodes to accelerate the optimization process. We consider a synchronous and an asynchronous parallel MOEA as a master-slave parallelization scheme for ELMOEA/D. We carry out an experiment with multi-objective optimization problems to compare the synchronous parallel ELMOEA/D with the asynchronous parallel ELMOEA/D. In the experiment, we simulate two settings of the evaluation time of solutions. One determines the evaluation time of solutions by the normal distribution with different variances. On the other hand, another evaluation time correlates to the objective function value. We compare the quality of solutions obtained by the parallel ELMOEA/D variants within a particular computing time. The experimental results show that the parallelization of ELMOEA/D significantly reduces the computational time. In addition, the integration of ELMOEA/D with the asynchronous parallelization scheme obtains higher quality of solutions quicker than the synchronous parallel ELMOEA/D.

Optimization of Vehicle-Borne Radar Antenna Pedestal Based on Modified NSGA-II

2014 ◽  
Vol 945-949 ◽  
pp. 2241-2247
Author(s):  
De Gao Zhao ◽  
Qiang Li
Keyword(s):  
Pareto Front ◽  
Optimization Problems ◽  
Population Diversity ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sorting Process ◽  
Definition Of ◽  
Radar Antenna ◽  
Modified Algorithm

This paper deals with application of Non-dominated Sorting Genetic Algorithm with elitism (NSGA-II) to solve multi-objective optimization problems of designing a vehicle-borne radar antenna pedestal. Five technical improvements are proposed due to the disadvantages of NSGA-II. They are as follow: (1) presenting a new method to calculate the fitness of individuals in population; (2) renewing the definition of crowding distance; (3) introducing a threshold for choosing elitist; (4) reducing some redundant sorting process; (5) developing a self-adaptive arithmetic cross and mutation probability. The modified algorithm can lead to better population diversity than the original NSGA-II. Simulation results prove rationality and validity of the modified NSGA-II. A uniformly distributed Pareto front can be obtained by using the modified NSGA-II. Finally, a multi-objective problem of designing a vehicle-borne radar antenna pedestal is settled with the modified algorithm.

Study on Multi-Objective Optimization Design Method for SRHSC Beams

2011 ◽  
Vol 243-249 ◽  
pp. 362-365
Author(s):  
Shan Suo Zheng ◽  
Yi Hu ◽  
Qing Lin Tao ◽  
Zhi Qiang Li ◽  
Lei Li
Keyword(s):  
Optimization Design ◽  
Optimization Problems ◽  
Design Method ◽  
High Strength ◽  
Shear Capacity ◽  
Structure Design ◽  
Complex Method ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Design Variables

According to the thought of engineering optimization and the theory of composite structure design, the minimum of the project cost and the maximum of the diagonal shear capacity are taken as the optimization objectives whose importance can be adjusted by weighting factors and the linear weighted method is used to establish evaluation function which changes multi-objective into single-objective in optimization. Taking the various constraints into consideration and choosing the sensitive design variables, the mathematical model of the multi-objective optimization design for steel reinforced high-strength concrete beams is provided. Based on the optimization thought of the Complex Method, the nonlinear optimization problems are solved by MATLAB program.

Adaptively Allocating Constraint-Handling Techniques for Constrained Multi-objective Optimization Problems

2021 ◽  
pp. 2159032
Author(s):  
Ning Yang ◽  
Hai-Lin Liu
Keyword(s):  
Optimization Problems ◽  
Performance Measure ◽  
Experimental Results ◽  
The Other ◽  
Constraint Handling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Selection Operators ◽  
Handling Technique ◽  
Feasible Solutions

For solving constrained multi-objective optimization problems (CMOPs), an effective constraint-handling technique (CHT) is of great importance. Recently, many CHTs have been proposed for solving CMOPs. However, no single CHT can outperform all kinds of CMOPs. This paper proposes an algorithm, namely, ACHT-M2M, which adaptively allocates the existing CHTs in an M2M framework for solving CMOPs. To be more specific, a CMOP is first decomposed into several constrained multi-objective optimization subproblems by ACHT-M2M. Each subproblem has a subpopulation in a subregion. CHT for each subregion is adaptively allocated according to a proposed composite performance measure. Population for the next generation is selected from subregions by selection operators with different CHTs and the obtained nondominated feasible solutions in each generation are used to update a predefined archive. ACHT-M2M assembles the advantages of different CHTs and makes them cooperate with each other. The proposed ACHT-M2M is finally compared with the other 12 representative algorithms on benchmark CMOPs and the experimental results further confirm the effectiveness of ACHT-M2M for solving CMOPs.

scholarly journals An Expensive Multi-Objective Optimization Algorithm Based on Decision Space Compression

2021 ◽  
pp. 2159039
Author(s):  
Haosen Liu ◽  
Fangqing Gu ◽  
Yiu-Ming Cheung
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Experimental Studies ◽  
Surrogate Models ◽  
Multi Objective Optimization ◽  
Decision Space ◽  
Multi Objective ◽  
The Past ◽  
Decision Variables ◽  
Space Compression

Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space.

scholarly journals Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

2021 ◽  
Vol 13 (4) ◽  
pp. 1929
Author(s):  
Yongmao Xiao ◽  
Wei Yan ◽  
Ruping Wang ◽  
Zhigang Jiang ◽  
Ying Liu
Keyword(s):  
Optimization Model ◽  
Optimization Design ◽  
Design Method ◽  
Low Cost ◽  
Route Optimization ◽  
Low Carbon ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Blank Design ◽  
Process Route

The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

Sign in / Sign up

Export Citation Format

Share Document