2017 IEEE Congress on Evolutionary Computation (CEC)最新文献_第7页

Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem 利用局部搜索策略改进NSGA-II多准则最小生成树问题的性能

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969432

J. Parraga-Alava, M. Dorn, Mario Inostroza-Ponta

{"title":"Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem","authors":"J. Parraga-Alava, M. Dorn, Mario Inostroza-Ponta","doi":"10.1109/CEC.2017.7969432","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969432","url":null,"abstract":"Finding a solution to the Multi-Criteria Minimum Spanning Tree (mc-MST) problem has direct benefit on real world problems. The Multi-objective Evolutionary Algorithm (MOEA) called NSGA-II (Non-Dominated Sorting Genetic Algorithm) has demonstrated to be the most promising approach to tackle mc-MST problem because of their efficiency and simplicity of implementation. However, it often reaches premature convergence and gets stuck at local optima causing the non-diversity of the population. To tackle this situation, the use local search strategies together with MOEAs has shown to be a good alternative. In this paper, we investigate the potential of local search methods to improve the overall effectiveness of NSGA-II to settle the mc-MST problem. We evaluate the performance of three general purpose local searches (Pareto Local Search, Tabu Search and Path Relinking) adapted to the multi-objective approach. Experimental results show that using Pareto Local Search (PLS) into the NSGA-II offers a better performance in terms of diversity and search space covered to settle the mc-MST problem.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129358204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

BLEAQ based solution for bilevel reliability-allocation problem 基于BLEAQ的双层可靠性分配问题求解方法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969630

Rahul Nath, Zubair Ashraf, Pranab K. Muhuri, Q. Lohani

{"title":"BLEAQ based solution for bilevel reliability-allocation problem","authors":"Rahul Nath, Zubair Ashraf, Pranab K. Muhuri, Q. Lohani","doi":"10.1109/CEC.2017.7969630","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969630","url":null,"abstract":"Reliability redundancy allocation problem (RRAP) is an optimization problem with objective to maximize the system reliability considering component reliability and redundancies as decision variables. RRAP was mostly solved as a single level optimization problem. However, the nature of the problem fits quite well in the framework of bilevel optimization. In this paper, we have proposed two novel bilevel formulations for the RRAP and solve them using a latest bilevel optimization algorithm called BLEAQ (bilevel evolutionary algorithm based on quadratic approximations). So far we knew no other research has been reported till date, where RRAP was addressed with bilevel optimization algorithm. Here, optimization is needed at two separate levels, where one problem is encircled within another problem. The inner problem is known as lower-level problem and the external problem is called upper-level problem. Here, we have presented two mixed-integer non-linear bilevel formulations for the RRAP of series-parallel system in a competitive environment. The purpose of the upper-level problem is to determine the component reliability that maximizes the total system reliability; whereas, lower-level problem minimizes the total cost (or weight) needed. We demonstrate the applicability of our approach with a suitable numerical example and show that our proposed approach works quite well than existing single level optimization tools.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132422357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Minimizing THD of multilevel inverters with optimal values of DC voltages and switching angles using LSHADE-EpSin algorithm 利用LSHADE-EpSin算法在直流电压和开关角的最优值下最小化多电平逆变器的THD

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969298

P. Biswas, Noor H. Awad, P. N. Suganthan, Mostafa Z. Ali, G. Amaratunga

{"title":"Minimizing THD of multilevel inverters with optimal values of DC voltages and switching angles using LSHADE-EpSin algorithm","authors":"P. Biswas, Noor H. Awad, P. N. Suganthan, Mostafa Z. Ali, G. Amaratunga","doi":"10.1109/CEC.2017.7969298","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969298","url":null,"abstract":"Multilevel inverters are mainly used for DC to AC power conversion and these inverters can be classified into types current source inverter (CSI) and voltage source inverter (VSI). Voltage source inverters are more common in power industry to convert lower levels of DC voltages into higher levels of AC voltages. In the process of conversion widely implemented pulse width modulated (PWM) switching technique of DC sources introduces harmonics in inverter output voltage. Total harmonic distortion (THD) is a measure of harmonic pollution in the power system and it is observed that variations in both DC voltages and switching angles of inverter affect the THD of inverter output voltage. Cascaded multilevel symmetric inverters ideally have DC sources all equal and constant. This paper considers inverters where DC sources can be unequal, a justifiable and realistic supposition. Optimal values of DC voltages and switching angles, which minimize THD level, are found using evolutionary algorithm. An advanced form of Differential Evolution (DE), called LSHADE-EpSin, is applied for the optimization problem. SHADE is a success history based parameter adaptation technique of DE. LSHADE improves the performance of SHADE with linearly reducing the population size in successive generations. LSHADE-EpSin introduces an additional adaptation technique for control parameters of the evolutionary algorithm. The algorithm has successfully been implemented for higher levels of inverters considered in the scope of our research study.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130489643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Dynamic Distance Minimization Problems for dynamic multi-objective optimization 动态多目标优化的动态距离最小化问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969411

Heiner Zille, Andre Kottenhahn, Sanaz Mostaghim

引用次数: 6

Clustering and differential evolution for multimodal optimization 多模态优化的聚类与差分进化

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969378

B. Bošković, J. Brest

{"title":"Clustering and differential evolution for multimodal optimization","authors":"B. Bošković, J. Brest","doi":"10.1109/CEC.2017.7969378","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969378","url":null,"abstract":"This paper presents a new differential evolution algorithm for multimodal optimization that uses self-adaptive parameter control, clustering and crowding methods. The algorithm includes a new clustering mechanism that is based on small subpopulations with the best strategy and, as such, improves the algorithm's efficiency. Each subpopulation is generated according to the best individual from a population that is not added to any other subpopulation. These small subpopulations are also used to determine population size and to replace ‘bad’ individuals. Because of the small subpopulation size and crowding mechanism, bad individuals prevent the best individuals from converging to the optimum. Therefore, the algorithm is trying to replace bad individuals with the individuals that are close to the best individuals. The population size expansion is used within the algorithm according to the number of generated subpopulations and located optima. The proposed algorithm was tested on benchmark functions for CEC'2013 special session and competition on niching methods for multimodal function optimization. The performance of the proposed algorithm was comparable with the state-of-the-art algorithms.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"317 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115869305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Gravitational search algorithm with linearly decreasing gravitational constant for parameter estimation of photovoltaic cells 线性减小引力常数的光伏电池参数估计的引力搜索算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969293

A. R. Jordehi

{"title":"Gravitational search algorithm with linearly decreasing gravitational constant for parameter estimation of photovoltaic cells","authors":"A. R. Jordehi","doi":"10.1109/CEC.2017.7969293","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969293","url":null,"abstract":"Due to undeniable environmental, economical and technical reasons, renewable energy-based power generation in electric power systems is continually increasing. Among renewables, photovoltaic (PV) power generation is a viable and attractive choice. For modeling photovoltaic systems, accurate modeling of PV cells is a must. PV cells are often modeled as single diode or double diode models. The process of estimating circuit model parameters of PV cells based on datasheet information or experimental I–V measurements is called PV cell parameter estimation problem and is being frequently researched in the last three decades. The research effort is being put to achieve more accurate circuit model parameters. In this paper, gravitational search algorithm (GSA) with linearly decreasing gravitational constant is proposed for solving PV cell parameter estimation problem. The results of application of the proposed GSA to PV cell parameter estimation problem vividly show its outperformance over GSA with constant gravitational constant, GSA with exponentially decreasing gravitational constant, genetic algorithm, evolutionary programming and Newton algorithm.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123202886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Knowledge-based Genetic Algorithm for the 0–1 Multidimensional Knapsack Problem 基于知识的遗传算法求解0-1多维背包问题

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969550

A. Rezoug, M. Bader-El-Den, D. Boughaci

{"title":"Knowledge-based Genetic Algorithm for the 0–1 Multidimensional Knapsack Problem","authors":"A. Rezoug, M. Bader-El-Den, D. Boughaci","doi":"10.1109/CEC.2017.7969550","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969550","url":null,"abstract":"This paper presents an improved version of Genetic Algorithm (GA) to solve the 0–1 Multidimensional Knapsack Problem (MKP01), which is a well-known NP-hard combinatorial optimisation problem. In combinatorial optimisation problems, the best solutions have usually a common partial structure. For MKP01, this structure contains the items with a high values and low weights. The proposed algorithm called Genetic Algorithm Guided by Pretreatment information (GAGP) calculates these items and uses this information to guide the search process. Therefore, GAGP is divided into two steps, in the first, a greedy algorithm based on the efficiency of each item determines the subset of items that are likely to appear in the best solutions. In the second, this knowledge is utilised to guide the GA process. Strategies to generate the initial population and calculate the fitness function of the GA are proposed based on the pretreatment information. Also, an operator to update the efficiency of each item is suggested. The pretreatment information has been investigated using the CPLEX deterministic optimiser. In addition, GAGP has been examined on the most used MKP01 data-sets, and compared to several other approaches. The obtained results showed that the pretreatment succeeded to extract the most part of the important information. It has been shown, that GAGP is a simple but very competitive solution.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124843455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Enhancing the robustness of complex networks against edge-based-attack cascading failures 增强复杂网络对边缘攻击级联故障的鲁棒性

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969291

Shuai Wang, Jing Liu

引用次数: 9

Evolutionary algorithm with convergence speed controller for automated software test data generation problem 基于收敛速度控制器的软件测试数据自动生成进化算法

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969400

Fangqing Liu, Han Huang, Z. Hao

引用次数: 1

Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony 不同异步度下并行异步多目标进化算法的性能比较

2017 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2017-06-05 DOI: 10.1109/CEC.2017.7969444

Tomohiro Harada, K. Takadama

{"title":"Performance comparison of parallel asynchronous multi-objective evolutionary algorithm with different asynchrony","authors":"Tomohiro Harada, K. Takadama","doi":"10.1109/CEC.2017.7969444","DOIUrl":"https://doi.org/10.1109/CEC.2017.7969444","url":null,"abstract":"This paper proposes a parallel asynchronous evolutionary algorithm (EA) with different asynchrony and verifies its effectiveness on multi-objective optimization problems. We represent such EA with different asynchrony as semi-asynchronous EA. The semi-asynchronous EA continuously evolves solutions whenever a part of solutions in the population completes their evaluations in the master-slave parallel computation environment, unlike a conventional synchronous EA, which waits for evaluations of all solutions to generate next population. To establish the semi-asynchronous EA, this paper proposes the asynchrony parameter to decide how many solutions are waited, and clarifies the effectual asynchrony related to the number of slave nodes. In the experiment, we apply the semi-asynchronous EA to NSGA-II, which is a well-known multi-objective evolutionary algorithm, and the semi-asynchronous NSGA-IIs with different asynchrony are compared with synchronous one on the multi-objective optimization benchmark problems with several variances of evaluation time. The experimental result reveals that the semi-asynchronous NSGA-II with low asynchrony has possibility to perform the best search ability than the complete asynchronous and the synchronous NSGA-II in the optimization problems with large variance of evaluation time.","PeriodicalId":335123,"journal":{"name":"2017 IEEE Congress on Evolutionary Computation (CEC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128425721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15