2015 IEEE Congress on Evolutionary Computation (CEC)最新文献_第7页

A math-hyper-heuristic approach for large-scale vehicle routing problems with time windows 带时间窗的大规模车辆路径问题的超启发式数学方法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256977

Nasser R. Sabar, Xiuzhen Zhang, A. Song

引用次数: 23

Reference point based distributed computing for multiobjective optimization 基于参考点的分布式计算多目标优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257250

Okkes Tolga Altinöz, K. Deb, A. Yılmaz

引用次数: 4

Development of a Genetic Algorithm based electric vehicle charging coordination on distribution networks 基于遗传算法的配电网电动汽车充电协调研究

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256903

Yen-Chih Yeh, M. Tsai

引用次数: 9

Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for large scale global optimization 大规模全局优化的高斯跳跃异构协同裸骨架粒子群优化实验结果

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257128

Joon-Woo Lee, Taeyong Choi, Hyunmin Do, Dongil Park, Chanhun Park, Youngsoo Son

{"title":"Experimental results of heterogeneous cooperative Bare Bones Particle Swarm Optimization with Gaussian jump for large scale global optimization","authors":"Joon-Woo Lee, Taeyong Choi, Hyunmin Do, Dongil Park, Chanhun Park, Youngsoo Son","doi":"10.1109/CEC.2015.7257128","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257128","url":null,"abstract":"Many optimization problems in recent engineering are complex and high-dimensional problems, a so-called Large-Scale Global Optimization (LSGO) problem, due to the increasing requirements for multidisciplinary approach. This paper proposes a novel Bare Bones Particle Swarm Optimization (BBPSO) algorithm to solve LSGO problems. The BBPSO is a variant of a Particle Swarm Optimization (PSO) and is based on Gaussian distribution. The BBPSO does not consider the selection of controllable parameters of the PSO and is a simple but powerful optimizer. This algorithm, however, is vulnerable to LSGO problems. This study has improved its performance for LSGO problems by combining the heterogeneous cooperation based on the information exchange between particles and the Gaussian jump strategy to avoid local optima. The CEC'2015 Special Session on Large-Scale Global Optimization has given 15 benchmark problems to provide convenience and flexibility for comparing various optimization algorithms specifically designed for large-scale global optimization. Simulations performed with those benchmark problems have verified the performance of the proposed optimizer and compared with the reference algorithm DECC-G of the CEC'2015 special session on LSGO.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132269039","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: 3

Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGA-II and adaptive MOEA/D: A comparison study 基于自适应NSGA-II和自适应MOEA/D求解多目标多阶段武器目标分配问题的比较研究

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257280

Juan Li, Jie Chen, Bin Xin, L. Dou

{"title":"Solving multi-objective multi-stage weapon target assignment problem via adaptive NSGA-II and adaptive MOEA/D: A comparison study","authors":"Juan Li, Jie Chen, Bin Xin, L. Dou","doi":"10.1109/CEC.2015.7257280","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257280","url":null,"abstract":"The weapon target assignment (WTA) problem is a fundamental problem arising in defense-related applications of operations research, and the multi-stage weapon target assignment (MWTA) problem is the basis of dynamic weapon target assignment (DWTA) problems which commonly exist in practice. The MWTA problem considered in this paper is formulated into a multi-objective constrained combinatorial optimization problem with two competing objectives. Apart from maximizing damage to hostile targets, this paper follows the principle of minimizing ammunition consumption under the consideration of resource constraints, feasibility constraints and fire transfer constraints. In order to tackle the two challenges, two types of multi-objective optimizers: NSGA-II (domination-based) and MOEA/D (decomposition-based) enhanced with an adaptive mechanism are adopted to achieve efficient problem solving. Then a comparison study between adaptive NSGA-II (ANSGA-II) and adaptive MOEA/D (AMOEA/D) on solving instances of three scales MWTA problems is done, and four performance metrics are used to evaluate each algorithm. Numerical results show that ANSGA-II outperforms AMOEA/D on solving multi-objective MWTA problems discussed in this paper, and the adaptive mechanism definitely enhances performances of both algorithms.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130414716","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: 34

Unconstrained robust optimization using a descent-based crossover operator 使用基于下降的交叉算子的无约束鲁棒优化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256878

Ankur Sinha, Aleksi Porokka, P. Malo, K. Deb

引用次数: 3

Multi-objective cooperative neuro-evolution of recurrent neural networks for time series prediction 时间序列预测递归神经网络的多目标协同神经进化

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256880

Rohitash Chandra

引用次数: 4

Gene regulatory network inference using Michaelis-Menten kinetics 基于Michaelis-Menten动力学的基因调控网络推断

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257181

Ahammed Sherief Kizhakkethil Youseph, M. Chetty, G. Karmakar

引用次数: 5

Representation matters: Real-valued encodings for meander line RFID antennas 表示问题:弯曲线RFID天线的实值编码

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7257039

James Montgomery

{"title":"Representation matters: Real-valued encodings for meander line RFID antennas","authors":"James Montgomery","doi":"10.1109/CEC.2015.7257039","DOIUrl":"https://doi.org/10.1109/CEC.2015.7257039","url":null,"abstract":"Solution representation can have a large impact on the performance of heuristic solvers. When tackling bounded self-avoiding walk problems, such as the meander line RFID antenna design problem, solutions may be represented in terms of the absolute or relative direction of travel at each step. Encoding these instructions in a continuous space is required in order to apply continuous solvers, but also allows for an adaptive interpretation of each instruction that promotes longer paths. Using path length as a proxy for antenna quality, this work demonstrates that the adaptive solution representations outperform their non-adaptive counterparts, and that starting from a corner node in the square design space positively influences algorithm performance. The superior performance of a relative encoding over an absolute one is confirmed, in both the single objective of maximising path length and in a substantial investigation of the multi-objective antenna design problem. In the multi-objective case, simplifying the problem by fixing the antenna start node can assist the algorithm to perform well, but allowing the algorithm to evolve antennas starting from any point leads to more consistent results.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126401495","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

A learning automata-based particle swarm optimization algorithm for noisy environment 噪声环境下基于学习自动机的粒子群优化算法

2015 IEEE Congress on Evolutionary Computation (CEC) Pub Date : 2015-05-25 DOI: 10.1109/CEC.2015.7256885

Junqi Zhang, LinWei Xu, Ji Ma, Mengchu Zhou

{"title":"A learning automata-based particle swarm optimization algorithm for noisy environment","authors":"Junqi Zhang, LinWei Xu, Ji Ma, Mengchu Zhou","doi":"10.1109/CEC.2015.7256885","DOIUrl":"https://doi.org/10.1109/CEC.2015.7256885","url":null,"abstract":"Particle Swarm Optimization (PSO) is an outstanding evolutionary algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly in noisy environments. Some studies have addressed this issue by introducing a resampling method. Most existing methods allocate a fixed and predetermined budget of re-evaluations for every iteration, but cannot change the budget according to different environments adaptively. Our previous work proposed a PSO-LA to integrate PSO with a Learning Automaton (LA) variant. PSO-LA utilizes LA's flexible self-adaption and automatic learning capability to learn the budget allocation for each iteration. This work further improves PSO-LA by the introduction of a subset scheme based LA (subLA) into PSO to further increase the probability of correctly finding the best particle through the pursuit on the a subset of particles with better performance, yielding a new method called LAPSO. LAPSO does not record the historical global best solution but finds it from the subset learned by subLA to jump out of the trapped area that may have a false global best solution. It can also adaptively consume computing budgets for every particle per iteration and, accordingly, total iteration times. Through experiments on 20 large-scale benchmark functions subject to different levels of noise, this work convincingly shows that LAPSO outperforms the existing ones in both accuracy and convergence rate of the optimization problems in noisy environments.","PeriodicalId":403666,"journal":{"name":"2015 IEEE Congress on Evolutionary Computation (CEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121323381","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: 17