Nonlinear separation concerning E-optimal solution of constrained multi-objective optimization problems

In this paper, NSGA-Ⅱ is used to realize the dual-objective optimization and three-objective optimization of the solar-thermal photovoltaic hybrid power generation system; Compared with the optimal solution set of three-objective optimization, optimization based on technical and economic evaluation indicators belongs to the category of multi-objective optimization. It can be considered that NSGA-Ⅱ is very suitable for multi-objective optimization of solar-thermal photovoltaic hybrid power generation system and other similar multi-objective optimization problems.

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A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212426 ◽

2021 ◽

pp. 1-21

Author(s):

Xin Li ◽

Xiaoli Li ◽

Kang Wang

Keyword(s):

Evolutionary Algorithm ◽

Optimization Problems ◽

Optimal Solution ◽

Benchmark Problems ◽

Multi Objective Optimization ◽

Multi Objective ◽

Environmental Selection ◽

Optimal Solution Set ◽

Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

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

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch021 ◽

2017 ◽

pp. 491-521 ◽

Cited By ~ 2

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.2808 ◽

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.

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Genetic Algorithm for the Multi-Objective Optimization of Product Gene

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.116 ◽

2012 ◽

Vol 6-7 ◽

pp. 116-121

Author(s):

Qing Song Ai ◽

Zhou Liu ◽

Yan Wang

Keyword(s):

Genetic Algorithm ◽

Genetic Engineering ◽

Manufacturing Industry ◽

Optimization Problems ◽

Rapid Development ◽

Optimal Solution ◽

Adaptive Mutation ◽

Multi Objective Optimization ◽

Multi Objective ◽

Modified Genetic Algorithm

In order to adapt to the rapid development of the manufacturing industry, product genetic engineering arises at the historic moment. Finding the optimal solution under more than one decision variables of the solution set is becoming the most important problems that we should solve. In this paper, we proposed a modified genetic algorithm to solve gene product genetic engineering of multi-objective optimization problems. The new concepts such as matrix encoding, column crossover and adaptive mutation are proposed as well. Experimental results show that the modified genetic algorithm can find the optimal solutions and match the customer’s expectations in modern manufacture.

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Hybrid Weights-Utility and Taguchi Method for Multi-Objective Optimization Problems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.764-765.305 ◽

2015 ◽

Vol 764-765 ◽

pp. 305-308

Author(s):

Kuang Hung Hsien ◽

Shyh Chour Huang

Keyword(s):

Taguchi Method ◽

Engineering Design ◽

Optimization Problems ◽

Optimal Solution ◽

Multi Objective Optimization ◽

Design Problems ◽

Multi Objective ◽

Utility Concept ◽

Engineering Design Problems

In this paper, hybrid weights-utility and Taguchi method is proposed to solve multi-objective optimization problems. The new method combines the Taguchi method and the weights-utility concept. The weights of the objective function and overall utility values are very important for the weights-utility, and must be set correctly in order to obtain an optimal solution. Application of this method to engineering design problems is illustrated with the aid of one case study, and the result shows that the weights-utlity method is able to handle multi-objective optimization problems, with an optimal solution which better meets the demand of multi-objective optimization problems than the utility concept does.

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