Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

基于多目标优化策略的在线学习资源推荐方法

目前在线学习资源推荐较多采用单目标转化方法,推荐过程中对学习者偏好考虑相对不足,影响学习资源推荐精度.针对上述问题,文中提出基于多目标优化策略的在线学习资源推荐模型(MOSRAM),在学习者规划时间内,以同时获得学习者对学习资源类型偏好度最大和难度水平适应度最佳为优化目标,设计具有向邻居均值学习能力和探索新区域能力的多目标粒子群优化算法(NEMOPSO),提出以MOSRAM为核心的在线学习资源推荐方法(NEMOPSO-RA).不同问题规模下融合经典多目标优化算法的推荐方法对比实验表明,NEMOPSO-RA可以有效提高在线学习资源的推荐精度和推荐性能.     

A NEW HYBRID ALGORITHM FOR MULTI-OBJECTIVE DISTRIBUTION FEEDER RECONFIGURATION

This article proposes an efficient hybrid algorithm for multi-objective distribution feeder reconfiguration. The hybrid algorithm is based on the combination of discrete particle swarm optimization (DPSO), ant colony optimization (ACO), and fuzzy multi-objective approach called DPSO-ACO-F. The objective functions are to reduce real power losses, deviation of nodes voltage, the number of switching operations, and the balancing of the loads on the feeders. Since the objectives are not the same, it is not easy to solve the problem by traditional approaches that optimize a single objective. In the proposed algorithm, the objective functions are first modeled with fuzzy sets to calculate their imprecise nature and then the hybrid evolutionary algorithm is applied to determine the optimal solution. The feasibility of the proposed optimization algorithm is demonstrated and compared with the solutions obtained by other approaches over different distribution test systems.     

基于混合粒子群算法的锌电解过程能耗优化

针对锌电解过程能耗过高的情况,研究其能耗优化问题.根据电力部门实行的分时计价政策,建立以全天锌电解过程电能消耗和总用电费用为目标的锌电解过程多目标优化模型.提出一种带加速度调整的粒子群优化算法,当粒子陷入局部最优时,通过加速度策略增强种群速度,使算法获得持续搜索的能力,有效克服早熟收敛;并和Powell算法相结合构成新的混合粒子群算法,将粒子群算法的全局搜索能力与Powell算法的局部寻优能力有机结合起来.最后将该混合粒子群算法应用于所建优化模型的求解,获得优化生产方案.仿真结果证明了该算法的有效性.工业应用效果表明,按所得优化方案组织生产降低了电能消耗,减少了用电费用.     

A fuzzy particle swarm optimization algorithm for computer communication network topology design

Particle swarm optimization (PSO) is a powerful optimization technique that has been applied to solve a number of complex optimization problems. One such optimization problem is topology design of distributed local area networks (DLANs). The problem is defined as a multi-objective optimization problem requiring simultaneous optimization of monetary cost, average network delay, hop count between communicating nodes, and reliability under a set of constraints. This paper presents a multi-objective particle swarm optimization algorithm to efficiently solve the DLAN topology design problem. Fuzzy logic is incorporated in the PSO algorithm to handle the multi-objective nature of the problem. Specifically, a recently proposed fuzzy aggregation operator, namely the unified And-Or operator (Khan and Engelbrecht in Inf. Sci. 177: 2692–2711, 2007), is used to aggregate the objectives. The proposed fuzzy PSO (FPSO) algorithm is empirically evaluated through a preliminary sensitivity analysis of the PSO parameters. FPSO is also compared with fuzzy simulated annealing and fuzzy ant colony optimization algorithms. Results suggest that the fuzzy PSO is a suitable algorithm for solving the DLAN topology design problem.     

基于改进粒子群算法的配电网重构策略

针对有源配电网对安全可靠性的要求较高,而现有的配电网重构算法精度低、速度低的问题,提出了基于蛙跳分组思想的自适应惯性权重的全信息简化粒子群算法。首先,从降低网络有功功率损耗、提高电压稳定性、均衡馈线负荷三个角度考虑,建立配电网多目标数学模型;然后,通过基于Pareto支配原则,采用模糊隶属函数的标准化满意度将多目标转化为相同量纲、同一属性、相同数量级的单目标,弥补加权法带有主观性、量纲不统一的弊端;最后,为保证种群多样性,避免随机初始化产生大量不可行解,结合蚁群优化（ACO）算法随机生成树和改进粒子群算法制定出一种针对含分布式电源（DG）的多目标配电网重构策略。通过对含DG的IEEE33节点配电网系统仿真验证,实验结果表明,与标准粒子群优化（PSO）算法相比,该重构策略寻优效率提高了41.0%,与重构前相比,该重构策略降低配电网有功损耗41.47%,降低电压偏移指数57.0%,改善系统负荷均衡度31.25%。该重构策略有效提高了寻优精度,提高了寻优速度,从而提高了配电网运行的安全可靠性。     

改进的r支配高维多目标粒子群优化算法

高维多目标优化问题是广泛存在于实际应用中的复杂优化问题,目前的研究方法大都限于进化算法.本文利用粒子群优化算法求解高维多目标优化问题,提出了一种基于r支配的多目标粒子群优化算法.采用r支配关系进行粒子的比较与选择,并结合粒子群优化算法收敛速度快的优势,使得算法在目标个数增加时仍保持较强的搜索能力;为了弥补由此造成的群体多样性的丢失,优化非r支配阈值的取值策略;此外,引入决策空间的拥挤距离测度,并给出新的外部存储器更新方法,从而进一步防止算法陷入局部最优.对多个基准测试函数的仿真结果表明所得解集在收敛性、多样性以及围绕参考点的分布性上均优于其他两种算法.     

多子群协同进化的多目标微粒群优化算法

微粒群优化(PSO)算法是一种非常有竞争力的求解多目标优化问题的群智能算法,因其容易陷入局部极值,导致非劣解集的收敛性和正确性不理想。为此提出一种基于多目标分解进化策略的多子群协同进化的多目标微粒群优化算法(MOPSO_MC),算法中每个子群对应于一个多目标分解之后的子问题,并构造了一种新的速率更新策略,每个粒子跟踪自身历史最优值、子群最优值和子群邻域最优值,从而在增强算法的局部寻优能力的同时,也能从邻域子群获得进化信息,实现协同进化。最后通过仿真实验,与现在主流的多目标微粒群算法在ZDT基准测试函数上比较,验证了算法的收敛性,解分布的均匀性和正确性。     

多目标粒子群算法的动态多燃料经济环境负荷分配

考虑动态的负荷需求和多种燃料资源,以经济成本和环境成本为优化指标,建立动态多燃料经济环境负荷分配的多目标优化模型,并提出一种多目标粒子群优化算法求解该类优化模型.模型采用动态负荷需求和多种燃料资源,更有利于节约电能成本和提高能源利用效率,但高维数、复杂非线性和多目标成为求解该优化模型的难点,故在算法中引入多目标解集更新策略和变邻域搜索策略.实验仿真结果表明,该模型是有效的,且采用所提算法求解这类模型时所获得的近似Pareto前端的精度明显优于其他算法.     

Handling multi-objective optimization problems with a multi-swarm cooperative particle swarm optimizer

This paper presents a new multi-objective optimization algorithm in which multi-swarm cooperative strategy is incorporated into particle swarm optimization algorithm, called multi-swarm cooperative multi-objective particle swarm optimizer (MC-MOPSO). This algorithm consists of multiple slave swarms and one master swarm. Each slave swarm is designed to optimize one objective function of the multi-objective problem in order to find out all the non-dominated optima of this objective function. In order to produce a well distributed Pareto front, the master swarm is developed to cover gaps among non-dominated optima by using a local MOPSO algorithm. Moreover, in order to strengthen the capability locating multiple optima of the PSO, several improved techniques such as the Pareto dominance-based species technique and the escape strategy of mature species are introduced. The simulation results indicate that our algorithm is highly competitive to solving the multi-objective optimization problems.     

Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem

This paper introduces a new algorithmic nature-inspired approach that uses particle swarm optimization (PSO) with different neighborhood topologies, for successfully solving one of the most computationally complex problems, the permutation flowshop scheduling problem (PFSP). The PFSP belongs to the class of combinatorial optimization problems characterized as NP-hard and, thus, heuristic and metaheuristic techniques have been used in order to find high quality solutions in reasonable computational time. The proposed algorithm for the solution of the PFSP, the PSO with expanding neighborhood topology, combines a PSO algorithm, the variable neighborhood search strategy and a path relinking strategy. As, in general, the structure of the social network affects strongly a PSO algorithm, the proposed method using an expanding neighborhood topology manages to increase the performance of the algorithm. As the algorithm starts from a small size neighborhood and by increasing (expanding) in each iteration the size of the neighborhood, it ends to a neighborhood that includes all the swarm, and it manages to take advantage of the exploration abilities of a global neighborhood structure and of the exploitation abilities of a local neighborhood structure. In order to test the effectiveness and the efficiency of the proposed method, we use a set of benchmark instances of different sizes and compare the proposed method with a number of other PSO algorithms and other algorithms from the literature.     

网络社区发现的多目标分解粒子群优化算法

通过分析社会网络中社区发现问题的优化目标,构造了社区发现的多目标优化模型,提出一种网络社区发现的多目标分解粒子群优化算法。该算法采用切比雪夫法将多目标优化问题分解为多个单目标优化子问题,使用粒子群优化(PSO)算法对社区结构进行挖掘,并引入了一种新颖的基于局部搜索的变异策略以提高算法的搜索效率和收敛速度,该算法克服了单目标优化算法存在的解单一以及难以发现社区层次结构的缺陷。人工网络及真实网络上的实验结果表明,该算法能够快速准确地挖掘网络社区并揭示社区的层次结构。     

Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems

In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem of steady-state of power systems. Specifically, reactive power control is formulated as a multi-objective optimization problem and solved using the parallel VEPSO algorithm. The results on the IEEE 30-bus test system are compared with those given by another multi-objective evolutionary technique demonstrating the advantage of parallel VEPSO. The parallel VEPSO is also tested on a larger power system this with 136 busses.     

基于改进型灰狼算法的RFID网络规划

随着物联网技术的飞速发展,射频识别(Radio Frequency Identification,RFID)系统因具有非接触、快速识别等优点而成为了解决物联网问题的首选方案.RFID网络规划问题要考虑多个目标,被证明是多目标优化的问题.群体智能(Swarm In-telligence,SI)算法在解决多目标优化问题方面...     

Attributed multi-objective comprehensive learning particle swarm optimization for optimal security of networks

In particle swarm optimization (PSO) each particle uses its personal and global or local best positions by linear summation. However, it is very time consuming to find the global or local best positions in case of complex problems. To overcome this problem, we propose a new multi-objective variant of PSO called attributed multi-objective comprehensive learning particle swarm optimizer (A-MOCLPSO). In this technique, we do not use global or local best positions to modify the velocity of a particle; instead, we use the best position of a randomly selected particle from the whole population to update the velocity of each dimension. This method not only increases the speed of the algorithm but also searches in more promising areas of the search space. We perform an extensive experimentation on well-known benchmark problems such as Schaffer (SCH), Kursawa (KUR), and Zitzler–Deb–Thiele (ZDT) functions. The experiments show very convincing results when the proposed technique is compared with existing versions of PSO known as multi-objective comprehensive learning particle swarm optimizer (MOCLPSO) and multi-objective particle swarm optimization (MOPSO), as well as non-dominated sorting genetic algorithm II (NSGA-II). As a case study, we apply our proposed A-MOCLPSO algorithm on an attack tree model for the security hardening problem of a networked system in order to optimize the total security cost and the residual damage, and provide diverse solutions for the problem. The results of our experiments show that the proposed algorithm outperforms the previous solutions obtained for the security hardening problem using NSGA-II, as well as MOCLPSO for the same problem. Hence, the proposed algorithm can be considered as a strong alternative to solve multi-objective optimization problems.     

Multi-objective colonial competitive algorithm for hybrid flowshop problem

This study analyses the multi-objective optimization in hybrid flowshop problem, in which two conflicting objectives, makespan and total weighted tardiness, are considered to be minimized simultaneously. The multi-objective version of Colonial Competitive Algorithm (CCA) for real world optimization problem is introduced and investigated. In contrast to multi-objective problems solved by CCA, presented in the literature, which used the combination of the objectives as single objective, the proposed algorithm is established on Pareto solutions concepts. Another novelty of this paper is estimating the power of each imperialist by a probabilistic criterion for this multi objective algorithm. Besides that, the variable neighborhood search is implemented as an assimilation strategy. Performance of the algorithm is finally compared with a famous algorithm for scheduling problem, NSGA-II, and the multi-objective form of CCA [28].     

An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is an extension of the classical job-shop scheduling problem. Although the traditional optimization algorithms could obtain preferable results in solving the mono-objective FJSP. However, they are very difficult to solve multi-objective FJSP very well. In this paper, a particle swarm optimization (PSO) algorithm and a tabu search (TS) algorithm are combined to solve the multi-objective FJSP with several conflicting and incommensurable objectives. PSO which integrates local search and global search scheme possesses high search efficiency. And, TS is a meta-heuristic which is designed for finding a near optimal solution of combinatorial optimization problems. Through reasonably hybridizing the two optimization algorithms, an effective hybrid approach for the multi-objective FJSP has been proposed. The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.     

改进果蝇优化算法在多目标搜索的应用

在实际工程优化问题中多数问题是多目标优化问题,多目标优化问题一直以来就是智能算法的研究热点。提出一种改进的果蝇优化算法,将其应用在多目标搜索领域,并成功使用该算法解决了一种多目标背包问题。算法在基本果蝇优化算法的基础上采用分群策略和动态半径,在群A中从种群位置开始以动态半径探索新的可行解,在群B中则通过非支配个体之间的交叉操作进行密集搜索。果蝇种群的位置在每一轮迭代产生的非劣解集中进行选取,提高了算法的收敛速度。通过在多个数据集下进行测试,并和粒子群算法、NSGA-2做了对比实验,最终结果显示使用该算法在特定条件下能取得较好的搜索效果,证明了使用果蝇优化算法解决多目标问题的可行性。     

Hybrid sliding level Taguchi-based particle swarm optimization for flowshop scheduling problems

A hybrid sliding level Taguchi-based particle swarm optimization (HSLTPSO) algorithm is proposed for solving multi-objective flowshop scheduling problems (FSPs). The proposed HSLTPSO integrates particle swarm optimization, sliding level Taguchi-based crossover, and elitist preservation strategy. The novel contribution of the proposed HSLTPSO is the use of a PSO to explore the optimal feasible region in macro-space, the use of a systematic reasoning mechanism of the sliding level Taguchi-based crossover to exploit the better solution in micro-space, and the use of the elitist preservation strategy to retain the best particles of multi-objective population for next iteration. The sliding level Taguchi-based crossover is embedded in the PSO to find the best solutions and consequently enhance the PSO. Using the systematic reasoning way of the Taguchi-based crossover with considering the influence of tuning factors α, β and γ is presented in this study to solve the conflicting problem of non-feasible solutions and to find the better particles. As a result, it exhibits a significant improvement in Pareto best solutions of the FSP. By combining the advantages of exploration and exploitation, from the computational experiments of the six test problems, the HSLTPSO provides better results compared to the existing methods reported in the literature when solving multi-objective FSPs. Therefore, the HSLTPSO is an effective approach in solving multi-objective FSPs.     

柔性车间调度的解空间距离聚类和变邻域搜索粒子群算法

根据柔性车间调度问题提出基于解空间距离聚类和变邻域搜索的粒子群算法.在粒子群算法基础上采用贪婪策略引入变邻域搜索方式,即调整关键路径上最大关键工序的机器位置,调整关键路径上工序相对位置变化,加强局部搜索能力;根据机器加工工序的空间距离,采用K-means聚类得到机器加工工序“优良个体”,加大局部搜索性能.同时对于粒子群算法速度更新采用局部停滞策略,保留局部片段相对位置不变特性.通过实验仿真,优化算法取得了较好的效果,与一般的粒子群算法相比较收敛速度迅速且性能良好.