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Quadratic approximation-based hybrid Artificial Cooperative Search algorithm for economic emission load dispatch problems

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2017年第4期27卷 1-14页

作者： Turgut, Mert Sinan Demir, Guleser Kalayci Dokuz Eylul Univ Grad Sch Nat & Appl Sci Dept Mechatron Engn TR-35390 Izmir Turkey Dokuz Eylul Univ Dept Elect & Elect Engn TR-35390 Izmir Turkey

In this article, Artificial Cooperative Search (ACS) algorithm is incorporated with the quadratic approximation (QA) operator to solve the multi-objective economic emission load dispatch (EELD) problems with different generation units. ACS is a Swarm Intelligence-based metaheuristic algorithm, based on the interaction between prey and predator organisms in a habitat, which is effective at global search;however, it does not perform so well at exploring promising regions. The QA operator, on the other hand, is a non-derivative-based efficient local search method that finds the minimum of a quadratic hyperspace passing through three points in a D-dimensional space. Solving the EELD problems with the hybridized ACS-QA algorithm, as being proposed in the present article, leads to more accurate results with fewer function evaluations. Also, multi-objectivity of the problem is handled by transforming it into a single-objective problem by using the weighted sum method. The efficiency of the proposed ACS-QA algorithm is tested in comparison to the algorithms existing in literature by implementing it on six different benchmark optimization problems. Afterward, the proposed ACS-QA algorithm and the ACS algorithm are implemented on multi-objective EELD problems with different generation units. The results are compared with the solutions in literature utilizing different metaheuristic optimization algorithms. Both studies firmly showed that the ACS-QA algorithm is able to find more accurate results even though it uses fewer function evaluation calls.

关键词： Artificial Cooperative Search algorithm economic emission load dispatch multi-objective optimization problems quadratic approximation operator

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Handling shape optimization of superconducting cavities with DNMOGA

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COMPUTER PHYSICS COMMUNICATIONS 2024年 299卷

作者： Wang, Peilin Ye, Kuangkuang Hao, Xuerui Wang, Jike Wuhan Univ Inst Adv Studies Wuhan 430072 Peoples R China

Radiofrequency (RF) cavities hold immense importance in various accelerator applications, but their optimization poses significant challenges due to complex situations involved. In this study, a recently proposed multiobjective optimization algorithm is utilized to optimize the 325 MHz double spoke cavity, which is characterized by 38 geometric parameters and is one of the most complex cavities commonly used in accelerators. The algorithm utilized combines neural network dynamically to speed up convergence of MOGAs, and it is called DNMOGA. Remarkably, when comparing to two manually optimized cavities (MOCs) respectively, DNMOGA consistently produces some cavities that outperform the MOC in all indicators concerned. This result announces the robust generalization capability exhibited by DNMOGA, and further shows the possibility of designing cavities employing the state-of-art optimization algorithms instead of manual optimization processes completely.

关键词： multi-objective optimization problems Superconducting double spoke cavity multiobjectives genetic algorithms Neural network DNMOGA

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A Convolutional Neural Network-Based Surrogate Model for multi-objective optimization Evolutionary Algorithm Based on Decomposition

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 72卷

作者： Zhang, Tao Li, Fuzhang Zhao, Xin Qi, Wang Liu, Tianwei Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Sch Tianjin Int Engn Inst Tianjin 300072 Peoples R China

Evolutionary algorithms (EAs) show good performance in solving multi-objective optimization problems (MOPs). An EA needs to perform a substantial number of fitness evaluations. For the MOP with high complexity, the fit-ness evaluation functions are computationally expensive, making the evolutionary algorithms time-consuming. Surrogate-assisted evolutionary algorithms (SAEAs) that apply surrogate models instead of fitness exact evalua-tion functions have successfully reduced the computational complexity of fitness evaluations. However, because training a surrogate model requires a certain amount of calculation, a large amount of calculation is required by the SAEA to train multiple surrogate models. Furthermore, most existing surrogate models may not achieve desired evaluation accuracy when processing medium-dimensional and high-dimensional MOPs. This paper pro -poses a novel surrogate model. The surrogate model can be applied in multi-objective optimization evolutionary algorithm based on decomposition (MOEA/D), which is a classic decomposition-based multi-objective optimiza-tion algorithm. The surrogate model is designed based on the convolutional neural network structure, and it is called the multi-objective parallel fitness evaluation network (MPFEN). An MPFEN model contains multiple sub-networks which can be applied as the surrogate models. By training the MPFEN model, we can obtain all sur-rogate models required by a MOEA/D simultaneously without training each required surrogate model separately. Therefore, the amount of calculation of training surrogate models in a MOEA/D is reduced. The evaluation accu-racy of the MPFEN model is tested by experiments. The experimental results show that the evaluation accuracy of MPFEN model is higher than that of other classical surrogate models in most cases. By applying the MPFEN model, the solution quality of SAEA is also improved.

关键词： multi-objective optimization problems Surrogate-assisted evolutionary algorithms multi-objective parallel fitness evaluation network Convolutional neural network

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multi-objective proportional-integral-derivative optimization algorithm for parameters optimization of double-fed induction generator-based wind turbines

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APPLIED SOFT COMPUTING 2021年 110卷 107673-107673页

作者： Yin, Linfei Gao, Qi Guangxi Univ Coll Elect Engn Nanning 530004 Guangxi Peoples R China Guangxi Univ Inst Artificial Intelligence Nanning 530004 Guangxi Peoples R China

The meta-heuristic algorithm inspired by natural may reduce the optimization performance due to excessive imitation. This paper proposes a novel multi-objective proportional-integral-derivative optimization algorithm inspired by mathematical thought to provide a better non-dominated solution for multi-objective problems. The idea of the proportional-integral-derivative control algorithm is introduced to cooperate with multi-objective optimization problems for the first time. The proposed algorithm is employed to store and maintain non-dominated solutions. Two groups of controllers of the proposed algorithm are designed for the multi-objective optimization problems, i.e., exploitative controllers aim to obtain the local optimal solution;explorative controllers aim to obtain the global optimal solution. To verify the effectiveness of the multi-objective proportional-integral-derivative optimization algorithm, eight comparison algorithms are compared with eight benchmark functions;five comparison algorithms are compared under the multi-objective parameters optimization problem of double-fed induction generator-based wind turbines. The results of benchmark functions show that the multi-objective proportional-integral-derivative optimization algorithm has superior convergence performances and outperforms other comparison algorithms. The proposed algorithm has excellent optimization performance to obtain the minimum deviation of rotor speed and reactive power for the wind power system controller. (C) 2021 Elsevier B.V. All rights reserved.

关键词： multi-objective optimization problems multi-objective proportional-integral-derivative optimization algorithm Meta-heuristic algorithm Double-fed induction generator

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Integration method of large-scale photovoltaic system in distribution network based on improved multi-objective TLBO algorithm

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Liu, Shili Yang, Fan Li, Jianqing Cheng, Zhiyu Zhang, Tianzhong Cheng, Tingli Guo, Yuanjun State Grid Anhui Elect Power Co Ltd Econ Res Inst Hefei Peoples R China State Grid Anhui Elect Power Co LTD Hefei Peoples R China Hefei Univ Technol Sch Elect & Automat Engn Hefei 230000 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

The increasing integration of distributed generations brings great challenges to the power grid. In this paper, a distributed photovoltaic (PV) integration methodology in distribution network is established for large-scale PV penetration. Firstly, a PV integration model was formulated with the aim of maximizing PV integration capacity and enhancing the voltage profile. Specially, the PV large-scale integration model for county-wide promotion is proposed by considering various typical integration scenarios. Additionally, a novel improved multi-objective Teaching-Learning based optimization (TLBO) algorithm, namely, IM-TLBO, was proposed to seek an optimal Pareto front of the PV integration model. The IM-TLBO algorithm innovatively incorporates the elite reverse learning search strategy to enhance exploration in the solution space. Moreover, the differentiated teaching guided by optimal individual and central location is employed to improve the efficiency of the "teaching" process. Meanwhile, a cyclic crowded sort deletion based on crowding distance is developed to enhance the diversity of elite individuals and the distribution characteristics of the Pareto Frontier. Finally, the performance of IM-TLBO is tested in benchmark functions. Also, a simulation case in IEEE 33 bus system is performed to verify the proposed PV integration method. It is observed that the proposed method in this paper can not only realize the overall optimal integration of roof distributed PV, but also improve voltage profile. The results of IM-TLBO are compared to other classical algorithms, and it is shown that IM-TLBO outperformed them in terms of convergence, distribution and diversity.

关键词： distributed PV integration capacity voltage TLBO multi-objective optimization problems

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The adaptable Pareto set problem for facility location: A video game approach

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115682-115682页

作者： Vargas-Santiago, Mariano Monroy, Raul Zhang, Chi Ramirez-Marquez, Jose E. Leon-Velasco, Diana A. Sch Sci & Engn Tecnol Monterrey Carretera Lago Guadalupe Km 35 Estado De Mexico 52926 Mexico Beijing Univ Technol Sch Econ & Engn Beijing 100124 Peoples R China Stevens Inst Technol Sch Syst Enterprises Enterprise Sci & Engn Div Hoboken NJ 07030 USA Univ Autonoma Metropolitana Dept Math Cuajimalpa Mexico

Facility Location is a multi-objective optimization problem, where, given a set of demand centers, D, one is to determine how many facilities to open, in such a way that we satisfy the overall demand, while minimizing the sum of two types of costs: one associated to opening a facility, and the other to the distance from each demand center to the nearest facility that has been opened. There exist various techniques to solve it, including methods that are complete, and others that find a good solution, though not necessarily the optimal one. Current techniques, however, do not contemplate that there already is an initial configuration of the problem or that changes have occurred in the environment and so the existing solution must be adjusted to meet new requirements: they reformulate the optimization problem each time, starting from scratch. To fill in this gap, we introduce the adaptable Pareto set of the Facility Location Problem. In particular, we introduce a method that may apply either of two heuristics, namely: the incremental and the decremental heuristics, which deal with the case where it is necessary to increase (respectively, decrease) the number of facilities opened. To complete these heuristics, we provide a video game for crowdsourcing solutions to the adaptable Pareto set for the facility location problem;we have considered both cases, one where some facilities need to be added, and the other, where some facilities need to be removed. We have tested our two methods using the Swain dataset. Our experimental results show that our heuristics are competitive, when compared against both the optimal solution found through a complete method, and that approximated via a genetic algorithm. Further, our results show that video game players may obtain better solutions than those found heuristically, and that these solutions sometimes are similar to those found using a brute force algorithm.

关键词： Facility location problem multi-objective optimization problems Pareto set Heuristics Serious video games Crowdsourcing

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A multi objective-BSC model for new product development project portfolio selection

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 162卷 113757-113757页

作者： Abbasi, Darya Ashrafi, Maryam Ghodsypour, Seyed Hassan Amirkabir Univ Technol Dept Ind Engn & Management Syst Tehran Iran

The most significant factor for the survival of an enterprise under a high level of competition is new product development (NPD). As a result, selecting a potential NPD project portfolio to gain competitive advantage has become a major concern to enterprises. However, selection of an NPD project portfolio is intricate due to multiple selection criteria and factors. This study focuses on optimizing an NPD project portfolio selection problem. To this end, Balance Score Card (BSC) is employed as a comprehensive framework to define NPD project selection criteria. Afterward, a multi-objective mathematical model is formulated that attempts to maximize total outcome, to minimize total risk, and to maximize strategic advantages. Our proposed model also takes into account suppliers, consumer demands, and project interdependencies. Because of the NP-hardness of the proposed model, two multi-objective metaheuristic algorithms, multi-objective particle swarm optimization (MOPSO), and non dominated sorting genetic algorithm (NSGA-II) are applied to solve the proposed model. It should be noted that the performance of algorithms is evaluated using the epsilon-constraint method and enhanced using response surface methodology (RSM). Finally, several numerical examples of different sizes are generated to compare the performance of metaheuristic solution methods based on four comparing metrics. Computational results show that NSGA-II outperforms MOPSO in terms of all the evaluation metrics. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： New product development Project portfolio selection multi-objective optimization problems Non dominated sorting genetic algorithm multi-objective particle swarm optimization epsilon-Constraint method

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A Post-Pareto Approach for multi-objective Decision Making Using a Non-Uniform Weight Generator Method

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Procedia Computer Science 2012年 12卷 116-121页

作者： Victor M. Carrillo Heidi Taboada Computational Science Program. The University of Texas at El Paso 500 West University El Paso TX 79968-0521 United States of America Department of Industrial Manufacturing and Systems Engineering. The University of Texas at El Paso 500 West University El Paso TX 79968-0521 United States of America

There exist two general approaches to solve multiple objective problems. The first approach involves the aggregation of all the objective functions into a single composite objective function. Mathematical methods such as the weighted sum method, goal programming, or utility functions are methods that pertain to this general approach. The output of this method is a single solution. On the other hand, we have the multiple objective evolutionary algorithms that offer the decision maker a set of trade off solutions usually called non dominated solutions or, Pareto-optimal solutions. This set is usually very large and the decision maker faces the problem of reducing the size of this set to have a manageable number of solutions to analyze. This paper presents a post- Pareto approach to prune the non-dominated set of solutions obtained by multiple objective evolutionary algorithms. The proposed approach uses a non-uniform weight generator method to reduce the size of the Pareto-optimal set. A pair of examples is presented to show the performance of the method.

关键词： post-Pareto optimality decision making multi-objective optimization problems

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An IGD+ Performance Indicator Based Particle Swarm Optimizer For multi-objective optimization

An IGD+ Performance Indicator Based Particle Swarm Optimizer...

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第33届中国控制与决策会议

作者： Fei Li Shijian Dong Yuanqu Liu Zhengkun Shang School of Electrical and Information Engineering Anhui Unviersity of Technology AnHui Province Key Laboratory of Special Heavy Load Robot Anhui Unviersity of Technology Ma'anshan University School of Information and Control Engineering China University of Mining and Technology Department of Automation North China Electric Power University

Particle swarm optimizer（PSO） is suitable for solving multi-objective optimization problems（MOPs）.However,there are two main issues for any multi-objective particle swarm optimizers（MOPSOs）.The first issue is how to balance the convergence and *** second issue is how to enhance the exploitation and exploration during the evolutionary *** order to address these issues,an modified inverted generational distance（IGD） performance indicator based PSO（IGD-MOPSO） is *** external archive updating strategies based on the IGD indicator and the objective space decomposition method are proposed to select the evenly distributed non-dominated *** leader updated strategy of each particle is based on the IGD indicator value which is associated to the corresponding reference *** genetic operator is embedded into the evolutionary procedure to reset the position in order to help the particle jump out of the local *** have conducted the simulation on some related benchmark test *** experimental results have indicated that the proposed algorithm is competitive with some related algorithms.

关键词： IGD+ indicator Particle swarm optimization The external archive updated strategy objective space decomposition multi-objective optimization problems

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Fast method of constructing the non-dominated set: Arena's principle

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Gaojishu Tongxin/Chinese High Technology Letters 2005年第4期15卷 11-14页

作者： Kuang, Da Zheng, Jinhua Institute of Information Engineering Xiangtan University Xiangtan 411105 China

multi-objective Evolutionary Algorithm (MOEA) is used to solve multi-objective optimization problems (MOOPs). In order to improve the efficiency of MOEA, a fast method of constructing non-dominated set called Arena' s Principle is suggested. The time complexity of the Arena's Principle is better than that of Deb's and Jensen's on constructing non-dominated set. In the experiments, Arena's Principle is compared with Deb' and Jensen's approach. It is shown that the experimental results satisfy the theoretical analysis.

关键词： multi-objective evolutionary algorithm multi-objective optimization problems Non-dominated set Pareto-optimal front

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