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.     

一种应用于柔性作业车间成组调度问题的分级优化算法

提出一种求解柔性作业车间成组调度FGJSS(flexible grouped job-shop scheduling)问题的蚁群粒子群求解算法。算法采用主从递阶形式,主级为蚁群优化算法,选择零件加工设备;从级为粒子群优化算法,在主级零件加工设备约束下优化设备作业排序以实现流通时间最小的目标。算法中,以工序加工时间和设备承载的作业族数为启发式信息设计蚂蚁在工序可用设备间转移概率;以粒子向量优先权值和作业族号为依据设计解码方法实现设备上的成组作业排序。最后,通过仿真实验,验证了该算法的有效性。     

PROMOT: a FORTRAN program to scan protein sequences against a library of known motifs

Information about the three-dimensional structure or function of a newly determined protein sequence can be obtained if the protein is found to contain a characterized motif or pattern of residues. Recently a database (PROSITE) has been established that contains 337 known motifs encoded as a list of allowed residue types at specific positions along the sequence. PROMOT is a FORTRAN computer program that takes a protein sequence and examines if it contains any of the motifs in PROSITE. The program also extends the definitions of patterns beyond those used in PROSITE to provide a simple, yet flexible, method to scan either a PROSITE or a user-defined pattern against a protein sequence database.     

Simplified particle swarm algorithm based on nonlinear decrease extreme disturbance and Cauchy mutation

Abstract

Concerning the drawbacks that particle swarm optimisation algorithm is easy to fall into the local optima, and has low solution precision, the simplified particle algorithm which based on the nonlinear decrease extreme disturbance and Cauchy mutation is proposed. The algorithm simplifies particle updating formula, and uses logistic chaotic sequence to initialise the particle position, which can improve the global search ability of population; nonlinear decrease extreme disturbance strategy enhanced the diversity of the population and avoid the particles trapping in local optimum; a novel Cauchy mutation is used for the optimal particle variation to generate more optimal guiding particle movement. The experimental simulation on seven typical test functions shows that the proposed algorithm can effectively avoid falling into local optimal solution, the search speed and optimisation accuracy have improved significantly. The algorithm is suitable to solve the function optimisation problem.     

Applications of particle swarm optimisation in integrated process planning and scheduling

Integration of process planning and scheduling (IPPS) is an important research issue to achieve manufacturing planning optimisation. In both process planning and scheduling, vast search spaces and complex technical constraints are significant barriers to the effectiveness of the processes. In this paper, the IPPS problem has been developed as a combinatorial optimisation model, and a modern evolutionary algorithm, i.e., the particle swarm optimisation (PSO) algorithm, has been modified and applied to solve it effectively. Initial solutions are formed and encoded into particles of the PSO algorithm. The particles “fly” intelligently in the search space to achieve the best sequence according to the optimisation strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles’ movements to form a modified PSO algorithm. Case studies have been conducted to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results generated by the genetic algorithm (GA) and the simulated annealing (SA) algorithm, respectively, and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in both applications.     

基于改进惯性权重的粒子群优化算法

惯性权重是粒子群算法中平衡全局搜索和局部搜索能力的重要参数,提出了一种基于改进惯性权重的粒子群优化算法。该算法在进化初期采用基于不同粒子不同维的动态自适应惯性权重策略,加快收敛速度,在进化后期采用线性递减权重策略,同时为防止陷入局优,适时引入混沌变异增加种群多样性。对5个典型测试函数的测试结果表明,NPSO在收敛速度、收敛精度、稳定性和全局搜索能力等方面比线性权重PSO(LDIWPSO)均有很大程度上的提高。     

An improved particle swarm optimiser based on swarm success rate for global optimisation problems

Inertia weight is one of the control parameters that influences the performance of particle swarm optimisation (PSO) in the course of solving global optimisation problems, by striking a balance between exploration and exploitation. Among many inertia weight strategies that have been proposed in literature are chaotic descending inertia weight (CDIW) and chaotic random inertia weight (CRIW). These two strategies have been claimed to perform better than linear descending inertia weight (LDIW) and random inertia weight (RIW). Despite these successes, a closer look at their results reveals that the common problem of premature convergence associated with PSO algorithm still lingers. Motivated by the better performances of CDIW and CRIW, this paper proposed two new inertia weight strategies namely: swarm success rate descending inertia weight (SSRDIW) and swarm success rate random inertia weight (SSRRIW). These two strategies use swarm success rates as a feedback parameter. Efforts were made using the proposed inertia weight strategies with PSO to further improve the effectiveness of the algorithm in terms of convergence speed, global search ability and improved solution accuracy. The proposed PSO variants, SSRDIW_PSO and SSRRIW_PSO were validated using several benchmark unconstrained global optimisation test problems and their performances compared with LDIW-PSO, CDIW-PSO, RIW-PSO, CRIW-PSO and some other existing PSO variants. Empirical results showed that the proposed variants are more efficient.     

基于GAPSO混合算法的网格工作流调度研究

网格工作流调度关注大规模的资源和任务调度,是一个复杂且具有挑战性的问题,它影响着网格工作流执行成功与否以及效率的高低。提出了基于遗传粒子群(GAPSO)的混合算法,引用了特殊的适应度函数,设定了动态的交叉和变异概率,并提出了动态切换算法的方法。结合各自算法的优势,在算法运行初期利用遗传算法的全局搜索能力进行优化搜索,在后期利用粒子群较强的局部搜索能力加快收敛速度。仿真结果表明该算法在执行时间方面有一定的优越性,能更有效地解决网格工作流调度问题。     

基于寿命的粒子群算法研究

针对粒子群算法易陷入局部最优的缺陷,提出了一种具有寿命的PSO(LS-PSO),算法赋予gbest有限的寿命,并且根据其引导能力对寿命进行自适应调整。当gbest耗尽其寿命时,它将失去领导能力,并被一个新产生并经测试具有足够引导能力的粒子所代替,继续引导群体搜索解空间的不同区域,并在两个单峰标准测试函数和六个多峰标准测试函数上对算法进行了测试。结果表明,LS-PSO比传统PSO及改进算法CLPSO有更好的求解精度和收敛速度。     

Design of water distribution networks using accelerated momentum particle swarm optimisation technique

Optimisation of looped water distribution networks (WDNs) has been recognised as an NP-hard combinatorial problem which cannot be easily solved using traditional mathematical optimisation techniques. This article proposes the use of a new version of heuristic particle swarm optimisation (PSO) for solving this problem. In order to increase the convergence speed of the original PSO algorithm, some accelerated parameters are introduced to the velocity update equation. Furthermore, momentum parts are added to the PSO position updating formula to get away from trapping in local optimums. The new version of the PSO algorithm is called accelerated momentum particle swarm optimisation (AMPSO). The proposed AMPSO is then applied to solve WDN design problems. Some illustrative and comparative illustrative examples are presented to show the efficiency of the introduced AMPSO compared with some other heuristic algorithms.     

Non-linear system control using a recurrent fuzzy neural network based on improved particle swarm optimisation

This article introduces a recurrent fuzzy neural network based on improved particle swarm optimisation (IPSO) for non-linear system control. An IPSO method which consists of the modified evolutionary direction operator (MEDO) and the Particle Swarm Optimisation (PSO) is proposed in this article. A MEDO combining the evolutionary direction operator and the migration operation is also proposed. The MEDO will improve the global search solution. Experimental results have shown that the proposed IPSO method controls the magnetic levitation system and the planetary train type inverted pendulum system better than the traditional PSO and the genetic algorithm methods.     

A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems

Many real-world optimisation problems are both dynamic and multi-modal, which require an optimisation algorithm not only to find as many optima under a specific environment as possible, but also to track their moving trajectory over dynamic environments. To address this requirement, this article investigates a memetic computing approach based on particle swarm optimisation for dynamic multi-modal optimisation problems (DMMOPs). Within the framework of the proposed algorithm, a new speciation method is employed to locate and track multiple peaks and an adaptive local search method is also hybridised to accelerate the exploitation of species generated by the speciation method. In addition, a memory-based re-initialisation scheme is introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms taken from the literature. The experimental results show the efficiency of the proposed algorithm for DMMOPs.     

Seeker optimisation (human group optimisation) algorithm with chaos

Seeker optimisation algorithm (SOA), also referred to as human group metaheuristic optimisation algorithms form a very hot area of research, is an emerging population-based and gradient-free optimisation tool. It is inspired by searching behaviour of human beings in finding an optimal solution. The principal shortcoming of SOA is that it is easily trapped in local optima and consequently fails to achieve near-global solutions in complex optimisation problems. In an attempt to relieve this problem, in this article, chaos-based strategies are embedded into SOA. Five various chaotic-based SOA strategies with four different chaotic map functions are examined and the best strategy is chosen as the suitable chaotic scheme for SOA. The results of applying the proposed chaotic SOA to miscellaneous benchmark functions confirm that it provides accurate solutions. It surpasses basic SOA, genetic algorithm, gravitational search algorithm variant, cuckoo search optimisation algorithm, firefly swarm optimisation and harmony search the proposed chaos-based SOA is expected successfully solve complex engineering optimisation problems.     

Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018)

The evolutionary optimisation algorithms appeared as an effective alternative to conventional statistical methods that have certain limitations in optimising complex manufacturing processes. Considering works published in the last decade, this paper presents an analysis of the particle swarm optimisation (PSO) implementation in designing parameters of heterogeneous manufacturing processes, both conventional and emerging, new processes. The literature review and analysis was structured according to the complexity of the optimisation problem (single response and multiresponse problems), and the development of an objective function for PSO. The tuning of the PSO algorithm-specific parameters was analysed in detail. The PSO algorithm performance was benchmarked with the results of other methods, including evolutionary algorithms, in designing process parameters. The concerns in applying PSO for multiresponse manufacturing problems were highlighted, and recommendations for future research were drawn. Such a comprehensive review on the PSO application in optimising manufacturing processes, including the detailed discussion on the algorithm characteristics and benchmark with other optimisation procedures, has not been pursued so far. Therefore, this review analysis provides hands on information for researchers and engineers at one place, and it is believed that the findings could serve as a basis for the future research and implementation directions.     

Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems

Particle swarm optimisation (PSO) is a well-established optimisation algorithm inspired from flocking behaviour of birds. The big problem in PSO is that it suffers from premature convergence, that is, in complex optimisation problems, it may easily get trapped in local optima. In this paper, a new PSO variant, named as enhanced leader PSO (ELPSO), is proposed for mitigating premature convergence problem. ELPSO is mainly based on a five-staged successive mutation strategy which is applied to swarm leader at each iteration. The experimental results confirm that in all terms of accuracy, scalability and convergence rate, ELPSO performs well.     

Chaotic bat swarm optimisation (CBSO)

Bat swarm optimisation (BSO) is a novel heuristic optimisation algorithm that is being used for solving different global optimisation problems. The paramount problem in BSO is that it severely suffers from premature convergence problem, that is, BSO is easily trapped in local optima. In this paper, chaotic-based strategies are incorporated into BSO to mitigate this problem. Ergodicity and non-repetitious nature of chaotic functions can diversify the bats and mitigate premature convergence problem. Eleven different chaotic map functions along with various chaotic BSO strategies are investigated experimentally and the best one is chosen as the suitable chaotic strategy for BSO. The results of applying the proposed chaotic BSO to different benchmark functions vividly show that premature convergence problem has been mitigated efficiently. Actually, chaotic-based BSO significantly outperforms conventional BSO, cuckoo search optimisation (CSO), big bang-big crunch algorithm (BBBC), gravitational search algorithm (GSA) and genetic algorithm (GA).     

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.     

一种基于遗传算法改进的粒子群优化算法

在研究标准粒子群算法和遗传算法的基础上,介绍一种加入遗传选择,交叉算子以及变异算子的扩展算法,以提高粒子群算法摆脱局部极值点的能力,并且算法具有较快的收敛能力。     

Markerless human articulated tracking using hierarchical particle swarm optimisation

In this paper, we address markerless full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional non-linear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult non-linear optimisation problems. We show that a small number of particles achieves accuracy levels comparable with several recent algorithms. PSO initialises automatically, does not need a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter (PF), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF) using the computational framework provided by Balan et al. HPSO accuracy and consistency are better than PF and compare favourably with those of APF and PSAPF, outperforming it in sequences with sudden and fast motion. We also report an extensive experimental study of HPSO over ranges of values of its parameters.     

Parameter optimisation of support vector machine using mutant particle swarm optimisation for diagnosis of metal-oxide surge arrester conditions

This paper proposes an enhanced support vector machine (SVM), whose parameters are optimised by a novel mutant particle swarm optimisation (mutant PSO) algorithm to identify metal-oxide surge arrester conditions. The total leakage current and its resistive component under different arrester conditions are obtained and then are inputted into a multilayer SVM for the purpose of fault identification. Then, a mutant PSO-based technique is investigated to increase the classification accuracy as well as the training speed of the SVM classifier. The proposed technique has been tested on an actual data set obtained from Taipower Company to monitor five arrester operating conditions, including normal (N), pre-fault (A), tracking (T), abnormal (U) and degradation (D). Furthermore, to demonstrate the effectiveness of the proposed mutant PSO, the obtained results are compared to those obtained by using cross-validation method, genetic algorithm and particle swarm optimisation.