A Tournament-Based Competitive Coevolutionary Algorithm

For an efficient competitive coevolutionary algorithm, it is important that competing populations be capable of maintaining a coevolutionary balance and hence, continuing evolutionary arms race to increase the levels of complexity. We propose a competitive coevolutionary algorithm that combines the strategies of neighborhood-based evolution, entry fee exchange tournament competition (EFE-TC) and localized elitism. An emphasis is placed on analyzing the effects of these strategies on the performance of competitive coevolutionary algorithms. We have tested the proposed algorithm with two adversarial problems: sorting network and Nim game problems that have different characteristics. The experimental results show that the interacting effects of the strategies appear to promote a balanced evolution between host and parasite populations, which naturally leads them to keep on evolutionary arms race. Consequently, the proposed algorithm provides good quality solutions with a little computation time.     

基于黄金分割准则的混沌云粒子群算法

针对传统粒子群算法寻优精度不高、易早熟的缺点,提出了基于黄金分割评判准则的混沌云粒子群（CCGPSO）算法。该算法利用黄金分割评判准则,将粒子群按照适应度大小分为标准粒子、混沌云粒子、云粒子三个子群,分别进行不同的算法操作。黄金分割的引入使整个粒子群可以搜索到全部解空间,解决了标准粒子群算法易陷入局部最优解和寻优精度不高的问题。选取了四种典型函数测试,并与混沌云粒子群算法（CCPSO）比较。仿真结果表明CCGPSO具有较高的寻优精度和收敛速度。     

Handling many-objective optimisation problems with R2 indicator and decomposition-based particle swarm optimiser

An R2 indicator-based multi-objective particle swarm optimiser (R2-MOPSO) can obtain well-convergence and well-distributed solutions while solving two and three objectives optimisation problems. However, R2-MOPSO faces difficulty to tackle many-objective optimisation problems because balancing convergence and diversity is a key issue in high-dimensional objective space. In order to address this issue, this paper proposes a novel algorithm, named R2-MaPSO, which combines the R2 indicator and decomposition-based archiving pruning strategy into particle swarm optimiser for many-objective optimisation problems. The innovations of the proposed algorithm mainly contains three crucial factors: (1) A bi-level archiving maintenance approach based on the R2 indicator and objective space decomposition strategy is designed to balance convergence and diversity. (2) The global-best leader selection is based on the R2 indicator and the personal-best leader selection is based on the Pareto dominance. Meanwhile, the objective space decomposition leader selection adopts the feedback information from the bi-level archive. (3) A new velocity updated method is modified to enhance the exploration and exploitation ability. In addition, an elitist learning strategy and a smart Gaussian learning strategy are embedded into R2-MaPSO to help the algorithm jump out of the local optimal front. The performance of the proposed algorithm is validated and compared with some algorithms on a number of unconstraint benchmark problems, i.e. DTLZ1-DTLZ4, WFG test suites from 3 to 15 objectives. Experimental results have demonstrated a better performance of the proposed algorithm compared with several multi-objective particle swarm optimisers and multi-objective evolutionary algorithms for many-objective optimisation problems.     

基于局部搜索的反向学习竞争粒子群优化算法

为提升粒子群优化算法在复杂优化问题,特别是高维优化问题上的优化性能,提出一种基于Solis&Wets局部搜索的反向学习竞争粒子群优化算法(solis and wets-opposition based learning competitive particle swarm optimizer with local se...     

Determining the Natural Frequency of Cantilever Beams Using ANN and Heuristic Search

An artificial neural network (ANN) is used to model the frequency of the first mode, using the beam length, the moment of inertia, and the load applied on the beam as input parameters on a database of 100 samples. Three different heuristic optimization methods are used to train the ANN: genetic algorithm (GA), particle swarm optimization algorithm and imperialist competitive algorithm. The suitability of these algorithms in training ANN is determined based on accuracy and runtime performance. Results show that, in determining the natural frequency of cantilever beams, the ANN model trained using GA outperforms the other models in terms of accuracy.     

基于PSO和BP复合算法的模糊神经网络控制器

为了克服单独应用粒子群算法（PSO）或BP算法训练模糊神经网络控制器参数时存在的缺陷,提出了一种训练模糊神经网络参数的PSO＋BP算法。该算法将二者相结合,即在PSO算法中加入一个BP算子,以充分利用PSO算法的全局寻优能力和BP算法的局部搜索能力,从而更有效地提高其收敛速度、训练效率和提高该模糊神经网络控制器的控制效果。最后的仿真实验结果验证了该基于PSO＋BP复合算法的模糊神经网络控制器的有效性和可行性。     

带组织的粒子群优化同步并行算法

提出带组织的粒子群优化同步并行算法.粒子群优化算法是一种基于群体智能的演化算法,具有良好的优化性能.但由于群体的迅速收敛和多样性低,导致算法早熟收敛.带组织的粒子群优化同步并行算法虽然克服了早熟收敛问题,但无形中却增加了计算时间.结合已有的并行计算技术,构造出了该方法的同步并行计算算法,仿真试验证明并行算法具有更快的收敛速度.     

Solving the balance problem of massively multiplayer online role-playing games using coevolutionary programming

In massively multiplayer online role-playing games (MMORPGs), each race holds some attributes and skills. Each skill contains several abilities such as physical damage and hit rate. All those attributes and abilities are functions of the character's level, which are called Ability-Increasing Functions (AIFs). A well-balanced MMORPG is characterized by having a set of well-balanced AIFs. In this paper, we propose a coevolutionary design method, including integration with the modified probabilistic incremental program evolution (PIPE) and the cooperative coevolutionary algorithm (CCEA), to solve the balance problem of MMORPGs. Moreover, we construct a simplest turn-based game model and perform a series of experiments based on it. The results indicate that the proposed method is able to obtain a set of well-balanced AIFs more efficiently, compared with the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA) and the hybrid discrete particle swarm optimization (HDPSO) algorithm. The results also show that the performance of PIPE has been significantly improved through the modification works.     

Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma

This paper presents and investigates the application of coevolutionary training techniques based on particle swarm optimization (PSO) to evolve playing strategies for the nonzero sum problem of the iterated prisoner's dilemma (IPD). Three different coevolutionary PSO techniques are used, differing in the way that IPD strategies are presented: A neural network (NN) approach in which the NN is used to predict the next action, a binary PSO approach in which the particle represents a complete playing strategy, and finally, a novel approach that exploits the symmetrical structure of man-made strategies. The last technique uses a PSO algorithm as a function approximator to evolve a function that characterizes the dynamics of the IPD. These different PSO approaches are compared experimentally with one another, and with popular man-made strategies. The performance of these approaches is evaluated in both clean and noisy environments. Results indicate that NNs cooperate well, but may develop weak strategies that can cause catastrophic collapses. The binary PSO technique does not have the same deficiency, instead resulting in an overall state of equilibrium in which some strategies are allowed to exploit the population, but never dominate. The symmetry approach is not as successful as the binary PSO approach in maintaining cooperation in both noisy and noiseless environments-exhibiting selfish behavior against the benchmark strategies and depriving them of receiving almost any payoff. Overall, the PSO techniques are successful at generating a variety of strategies for use in the IPD, duplicating and improving on existing evolutionary IPD population observations.     

新型分阶段粒子群优化算法

针对粒子群优化算法的“早熟”问题,提出了一种新型分阶段粒子群优化算法。该算法通过调整惯性权重和加速系数使粒子自组织地跟踪局部吸引域和全局吸引域来扩大粒子的搜索空间和提高粒子的收敛精度,同时根据粒子处于不同的阶段实施相应的变异策略来增加种群的多样性。通过经典函数的测试结果表明,新算法的全局搜索能力有了显著提高,并且能够有效避免早熟问题。     

Learning to play games using a PSO-based competitive learning approach

A new competitive approach is developed for learning agents to play two-agent games. This approach uses particle swarm optimizers (PSO) to train neural networks to predict the desirability of states in the leaf nodes of a game tree. The new approach is applied to the TicTacToe game, and compared with the performance of an evolutionary approach. A performance criterion is defined to quantify performance against that of players making random moves. The results show that the new PSO-based approach performs well as compared with the evolutionary approach.     

纳什均衡解及其QPSO算法求解

纳什均衡是一种博弈的解的概念,可以对非常广泛类型的博弈作出严格的多的预测。具有量子行为的粒子群算法是一种能够较好的解决优化问题的算法,它是在粒子群算法的基础上发展起来的。本文讨论纳什均衡解,并利用QPSO算法来求解纳什均衡解。通过仿真算法及与几种算法的比较结果验证了算法的有效性,证明了算法的全局收敛性。     

基于博弈模型的合作式粒子群优化算法

粒子群算法作为一种新兴的进化优化方法,能够大大减轻复杂的大规模优化问题的计算负担. 根据博弈论的思想,在传统粒子群基础上提出了一种基于博弈模型的合作式粒子群优化算法,算法基于重复博弈模型,在重复博弈中利用一个博弈序列,使得每次博弈都能够产生最大效益,并得到了相应博弈过程的纳什均衡. 通过典型基准测试函数对算法的性能进行对比实验,实验结果表明算法是可行的、有效的,对拓展粒子群算法研究具有重要的理论意义与实际意义.     

基于改进量子粒子群算法的Nash均衡研究

在N人非合作博弈Nash均衡问题求解过程中,将量子不确定性原理、协同演化以及免疫算法内的抗体浓度抑制机制引进到经典粒子群算法中,设计了一种新型改进量子粒子群算法来更好地处理Nash均衡问题。该算法在运算过程中,运用抗体浓度以及协同演化的方式来维系粒子群具备的多样性特征,并借助量子不确定性缩减迭代搜索耗时。该算法不仅有效地将粒子群算法运算简单与方便实现的特质承继下来,而且算法的收敛速度以及其全局搜索能力都获得了大幅度的提升。相关数值算例分析表明,改进的算法能够更好地处理粒子早熟,相较遗传算法以及免疫粒子群算法更具性能优越性。     

关于无线传感器网络定位算法仿真

针对无线传感器网络(WSNs)节点定位的问题,提出了一种通过构建粒子群机制的量子神经网络模型优化距离矢量跳跃(DV-HOP)的定位算法(PSO-QNN),根据传统DV-HOP所得到的平均距离和实测节点距离构建量子神经网络模型,并通过粒子群算法对平均距离进行训练,从而得到较优平均值,实现了对DV-HOP算法的优化.算法缩短了传统人工神经网络的训练时间,并且加快了收敛速度.仿真结果表明:与传统DV-HOP算法相比,所提出的PSO-QNN算法能够减少约20%的定位误差,定位精度显著提高.     

Quantum immune clonal coevolutionary algorithm for dynamic multiobjective optimization

The existing algorithms to solve dynamic multiobjective optimization (DMO) problems generally have difficulties in non-uniformity, local optimality and non-convergence. Based on artificial immune system, quantum evolutionary computing and the strategy of co-evolution, a quantum immune clonal coevolutionary algorithm (QICCA) is proposed to solve DMO problems. The algorithm adopts entire cloning and evolves the theory of quantum to design a quantum updating operation, which improves the searching ability of the algorithm. Moreover, coevolutionary strategy is incorporated in global operation and coevolutionary competitive operation and coevolutionary cooperative operation are designed to improve the uniformity, the diversity and the convergence performance of the solutions. The results on test problems and performance metrics compared with ICADMO and DBM suggest that QICCA has obvious effectiveness and advantages which shows great capability of evolving convergent, diverse and uniformly distributed Pareto fronts.     

基于改进粒子群算法的支持向量机

对求解含线性约束优化问题的粒子群算法（LPSO）进行了改进,给出了应用其训练支持向量机（SVM）的方法。改进后的算法在基本PSO惯性权重策略的基础上加入了基于种群收敛速度的自适应扰动,能够较好地调整算法的全局与局部搜索能力之间的平衡。对双螺旋问题的分类实验表明本文提出的方法稳定性好,训练出的SVM具有较高的分类正确率。     

多自适应策略粒子群优化算法及应用

为了平衡粒子群优化算法的全局和局部搜索能力,提出了一种多自适应策略粒子群优化算法。该算法在粒子进化过程中,采用了基于粒子进化度和局部开启混沌搜索相结合的速度自适应调节策略。将算法应用于模拟电路故障诊断的BP神经网络训练中,有效地解决了常规BP算法收敛速度慢、易陷入局部极小的问题。仿真结果表明算法具有较快的收敛速度和较高的诊断精度。     

基于拓扑结构与粒子变异改进的粒子群优化算法

为使粒子群优化算法(PSO)优化过程的多样性与收敛性得到合理解决,以提高算法优化性能,基于种群拓扑结构与粒子变异提出两种粒子群改进算法RSMPSO和RVMPSO.改进算法将具有信息定向流动的闭环拓扑结构与星型拓扑结构或四边形拓扑结构相结合,促使粒子在前期寻优过程中具有较高的多样性,保证搜索的广度,而在后期满足粒子群的整体收敛性,保证寻优的精度.同时,将布谷鸟搜索算法(CS)中的偏好随机游走变异策略引入改进算法中,增强粒子跳出局部最优的能力.对标准测试函数的仿真实验表明,所改进的PSO算法与其他6个对比算法相比不仅操作简单,优化精度高,而且在算法收敛性及稳健性方面都有着更出色的表现.     

A new multi-objective particle swarm optimisation algorithm based on R2 indicator selection mechanism

In recent years, Pareto-based selection mechanism has been successfully applied in dealing with complex multi-objective optimisation problems (MOPs), while indicators-based have been explored to apply in solving this problems. Therefore, a new multi-objective particle swarm optimisation algorithm based on R2 indicator selection mechanism (R2SMMOPSO) is presented in this paper. In the proposed algorithm, R2 indicator is designed as a selection mechanism for ensuring convergence and distribution of the algorithm simultaneously. In addition, an improved cosine-adjusted inertia weight balances the ability of algorithm exploitation and exploration effectively. Besides, Gaussian mutation strategy is designed to prevent particles from falling into the local optimum when the particle does not satisfy the condition of the position update formula, polynomial mutation is applied in the external archive to increase the diversity of elite solutions. The performance of the proposed algorithm is validated and compared with some state-of-the-art algorithms on a number of test problems. Experimental studies demonstrate that the proposed algorithm shows very competitive performance when dealing with complex MOPs.