一种改进快速稳定的多目标优化算法

多目标优化问题属于高维的搜索空间,用一些传统方法来优化这些问题会导致较高的时间复杂性.为了解决该问题,使用了粒子群优化算法(PSO),同时将ε-dominance的概念应用到PSO中.该方法在实验过程中取得了良好的效果.其运算速度快,而且最终优化的点数可以得到控制.     

一种多目标粒子群改进算法的研究

针对多目标粒子群优化过程中的粒子飞行偏向性和多样性损失问题,提出一种基于最大最小适应函数的改进算法．该算法在最大最小适应函数的计算中引入了函数相对值算法和ε-支配的概念,并提出了变ε-支配的策略,改进了最大最小适应函数的计算方法,解决了粒子飞行过程中的偏向性和多样性损失问题,加快了算法的收敛速度．将该改进算法应用于直流变频压缩机启动时峰值电流和启动转速的优化问题,应用结果表明该算法收敛速度快且效果良好．

     

一种基于相角映射的改进多目标粒子群优化算法

提出一种相角粒子群优化算法求解多目标优化问题。该算法采用相角映射实现了粒子在相角空间上仅依赖于归一化多目标函数的快速搜索,在粒子飞行信息共享机制上引入共享池概念,提出基于关联支配排序和相似度排序的共享池更新策略,提高了Pareto解的多样性。采用Sigma领导策略和混沌变异操作,平衡了算法的快速搜索能力和全局寻优能力。标准多目标测试函数和电力系统广域阻尼控制多目标优化算例表明了所提出算法的可行性和有效性。     

基于最大最小适应度函数的多目标粒子群算法

针对多目标优化问题提出了一种基于最大最小适应度函数（F_maximin）的粒子群算法，将此算法简称为IMPSO。它在求解多目标问题的非劣解前沿（Pareto Front）时表现出很好的性能。通过经典测试函数计算表明该算法保证收敛到多目标优化问题的Pareto最优前沿；同时，使用两个性能指标（GD和Diversity）验证了此算法优于其他的多目标粒子群优化算法。     

多目标优化的粒子群算法研究

本文介绍了粒子群优化算法PSO中的多目标优化的粒子群算法及其应用,并将其运用在防守对方多个前锋球员的进攻威胁,以粒子群算法随机性来适应不断变化的形势。     

一种改进的粒子群多目标优化算法研究

为了解决多目标优化过程中各个解之间存在的资源争夺、冲突,算法由于趋同性而带来的早熟无法收敛等缺点,文中提出了一种多子种群协同优化粒子群算法。算法分别采用不同的种群优化不同的目标,并且在算法中引入外部档案和精英学习策略,使得算法能够得到更多的外部档案的解供选择,精英学习策略是为了使算法的分布性和收敛性更好。最后将算法应用到多目标测试函数中,通过实验验证了改进后的算法的收敛性和分布性都比经典多目标算法NSGA-II要好。     

一种改进的小生境多目标粒子群优化算法

提出一种小生境多目标粒子群优化算法。使用环邻域拓扑且无需任何小生境参数,克服常规小生境技术中需确定小生境参数的困难。采用NSGA-II的非支配排序策略和动态加权方法选择最优粒子。基于拥挤度的变异操作引导粒子跳出局部最优,增强算法的全局搜索能力。通过对ZDT1~ZDT4和ZDT6的测试结果表明,与经典的多目标进化算法NSGA-II、PESA-II和MOPSO相比,该算法在最优解集的收敛度与多样性方面具有明显的优势。     

一种基于粒子群算法的改进多目标文化算法

提出一种基于粒子群算法的改进多目标文化算法并用于求解多目标优化问题.算法中群体空间采用多目标粒子群优化算法进行演化;信念空间通过对形势知识、规范化知识和历史知识的重新定义使之符合多目标优化问题;信念空间和群体空间的交互通过自适应的接受操作和影响操作来实现.若干多目标标准测试函数的仿真结果表明,改进多目标文化算法能够在保持Pareto解集多样性的同时具有较好的均匀性和收敛性.     

基于融合多策略改进的多目标粒子群优化算法

为进一步提高多目标粒子群算法的收敛性和多样性,提出一种多策略融合改进的多目标粒子群优化算法.首先,引入分解思想以增加Pareto解集的多样性;然后,在速度和位置更新时,引入“多点”变异,即随着迭代次数的递增,根据相应判据对位置的更新作出不同的变异,避免算法早熟现象的发生;最后,将更新后种群和最优解集进行非支配排序,最优解放入精英外部存档.仿真实验结果表明,与另外4种进化算法对比,所提出算法表现出良好的整体性能.     

改进的多目标粒子群优化算法

为提高解决多目标优化问题的能力,提出一种改进的多目标粒子群优化算法。该算法采用均匀随机初始化方法初始种群,采用快速支配策略选取非支配解,生成外部档案;通过比较粒子连续几代的更新情况来判断是否陷入局部最优并相应地采取不同的更新策略,同时引入变异因子对粒子进行扰动。实验结果表明,在世代距离GD(Generational Distance)和空间评价方法 SP(Spacing)性能指标上,改进之后的算法与另外几种对等算法相比,具有显著的整体优势。     

基于拥挤度与变异的动态微粒群多目标优化算法

提出一种动态微粒群多目标优化算法(DCMOPSO),算法中的惯性权重和加速因子动态变化以增强算法的全局搜索能力,并采用拥挤度的方法对外部档案进行维护以增加非劣解的多样性.在维护过程中,从外部档案中按拥挤度为每个微粒选择全局最好位置,同时使用变异操作避免算法早熟.通过几个典型的多目标测试函数对DCMOPSO算法的性能进行了测试,并与多目标优化算法MOPSO和NSGA-Ⅱ进行对比.结果表明,DCMOPSO算法具有良好的搜索性能.     

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.     

A multi-objective particle swarm optimization for project selection problem

Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances.     

A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems

This paper proposes a self-organized speciation based multi-objective particle swarm optimizer (SS-MOPSO) to locate multiple Pareto optimal solutions for solving multimodal multi-objective problems. In the proposed method, the speciation strategy is used to form stable niches and these niches/subpopulations are optimized to search and maintain Pareto-optimal solutions in parallel. Moreover, a self-organized mechanism is proposed to improve the efficiency of the species formulation as well as the performance of the algorithm. To maintain the diversity of the solutions in both the decision and objective spaces, SS-MOPSO is incorporated with the non-dominated sorting scheme and special crowding distance techniques. The performance of SS-MOPSO is compared with a number of the state-of-the-art multi-objective optimization algorithms on fourteen test problems. Moreover, the proposed SS-MOSPO is also employed to solve a real-life problem. The experimental results suggest that the proposed algorithm is able to solve the multimodal multi-objective problems effectively and shows superior performance by finding more and better distributed Pareto solutions.     

A Pareto archive particle swarm optimization for multi-objective job shop scheduling

In this paper, we present a particle swarm optimization for multi-objective job shop scheduling problem. The objective is to simultaneously minimize makespan and total tardiness of jobs. By constructing the corresponding relation between real vector and the chromosome obtained by using priority rule-based representation method, job shop scheduling is converted into a continuous optimization problem. We then design a Pareto archive particle swarm optimization, in which the global best position selection is combined with the crowding measure-based archive maintenance. The proposed algorithm is evaluated on a set of benchmark problems and the computational results show that the proposed particle swarm optimization is capable of producing a number of high-quality Pareto optimal scheduling plans.     

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.     

Particle swarm optimization with query-based learning for multi-objective power contract problem

Particle swarm optimization (PSO) is one of the most important research topics on swarm intelligence. Existing PSO techniques, however, still contain some significant disadvantages. In this paper, we present a new QBL-PSO algorithm that uses QBL (query-based learning) to improve both the exploratory and exploitable capabilities of PSO. Here, we apply a QBL method proposed in our previous research to PSO, and then test this new algorithm on a real case study on problems of power conservation. Our algorithm not only broadens the search diversity of PSO, but also improves its precision. Conventional PSO often snag on local solutions when performing queries, instead of finding better global solutions. To resolve this limitation, when particles converge in nature, we direct some of them into an “ambiguous solution space” defined by our algorithm. This paper introduces two ways to invoke this QBL algorithm. Our experimental results confirm that the proposed method attains better convergence to the global best solution. Finally, we present a new PSO model for solving multi-objective power conservation problems. Overall, this model successfully reduces power consumption, and to our knowledge, this paper represents the first attempt within the literature to apply the QBL concept to PSO.     

基于局部搜索与混合多样性策略的多目标粒子群算法

为了提高算法的收敛性与非支配解集的多样性,提出一种基于局部搜索与混合多样性策略的多目标粒子群算法（LH-MOPSO）.该算法使用增广Lagrange乘子法对非支配解进行局部搜索以快速接近Pareto最优解;利用基于改进的Maximin适应值函数与拥挤距离的混合多样性策略对非支配解集进行维护以保留解的多样性,同时引入高斯变异算子以避免算法早熟收敛;最后针对多目标约束优化问题,给出一种有效的约束处理方法.实验研究表明该算法具有良好的优化性能.     

A novel approach to combine features for salient object detection using constrained particle swarm optimization

Despite significant amount of research works, the best available visual attention models still lag far behind human performance in predicting salient object. In this paper, we present a novel approach to detect a salient object which involves two phases. In the first phase, three features such as multi-scale contrast, center-surround histogram and color spatial distribution are obtained as described in Liu et al. model. Constrained Particle Swarm Optimization is used in the second phase to determine an optimal weight vector to combine these features to obtain saliency map to distinguish a salient object from the image background. To achieve this, we defined a simple fitness function which highlights a salient object region with well-defined boundary and effectively suppresses the background regions in an image. The performance is evaluated both qualitatively and quantitatively on a publicly available dataset. Experimental results demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of precision, recall, F -measure and area under curve.     

Particle swarm optimization with preference order ranking for multi-objective optimization

A new optimality criterion based on preference order (PO) scheme is used to identify the best compromise in multi-objective particle swarm optimization (MOPSO). This scheme is more efficient than Pareto ranking scheme, especially when the number of objectives is very large. Meanwhile, a novel updating formula for the particle’s velocity is introduced to improve the search ability of the algorithm. The proposed algorithm has been compared with NSGA-II and other two MOPSO algorithms. The experimental results indicate that the proposed approach is effective on the highly complex multi-objective optimization problems.