A diverse human learning optimization algorithm

Human Learning Optimization is a simple but efficient meta-heuristic algorithm in which three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to efficiently search the optimal solution by imitating the learning mechanisms of human beings. However, HLO assumes that all the individuals possess the same learning ability, which is not true in a real human population as the IQ scores of humans, one of the most important indices of the learning ability of humans, follow Gaussian distribution and increase with the development of society and technology. Inspired by this fact, this paper proposes a Diverse Human Learning Optimization algorithm (DHLO), into which the Gaussian distribution and dynamic adjusting strategy are introduced. By adopting a set of Gaussian distributed parameter values instead of a constant to diversify the learning abilities of DHLO, the robustness of the algorithm is strengthened. In addition, by cooperating with the dynamic updating operation, DHLO can adjust to better parameter values and consequently enhances the global search ability of the algorithm. Finally, DHLO is applied to tackle the CEC05 benchmark functions as well as knapsack problems, and its performance is compared with the standard HLO as well as the other eight meta-heuristics, i.e. the Binary Differential Evolution, Simplified Binary Artificial Fish Swarm Algorithm, Adaptive Binary Harmony Search, Binary Gravitational Search Algorithms, Binary Bat Algorithms, Binary Artificial Bee Colony, Bi-Velocity Discrete Particle Swarm Optimization, and Modified Binary Particle Swarm Optimization. The experimental results show that the presented DHLO outperforms the other algorithms in terms of search accuracy and scalability.     

A novel memetic algorithm and its application to data clustering

In this paper, a novel memetic algorithm (MA) named GS-MPSO is proposed by combining a particle swarm optimization (PSO) with a Gaussian mutation operator and a Simulated Annealing (SA)-based local search operator. In GS-MPSO, the particles are organized as a ring lattice. The Gaussian mutation operator is applied to the stagnant particles to prevent GS-MPSO trapping into local optima. The SA-based local search strategy is developed to combine with the cognition-only PSO model and perform a fine-grained local search around the promising regions. The experimental results show that GS-MPSO is superior to some other variants of PSO with better performance on optimizing the benchmark functions when the computing resource is limited. Data clustering is studied as a real case study to further demonstrate its optimization ability and usability, too.     

Hybridizing local search algorithms for global optimization

In this paper, we combine two types of local search algorithms for global optimization of continuous functions. In the literature, most of the hybrid algorithms are produced by combination of a global optimization algorithm with a local search algorithm and the local search is used to improve the solution quality, not to explore the search space to find independently the global optimum. The focus of this research is on some simple and efficient hybrid algorithms by combining the Nelder–Mead simplex (NM) variants and the bidirectional random optimization (BRO) methods for optimization of continuous functions. The NM explores the whole search space to find some promising areas and then the BRO local search is entered to exploit optimal solution as accurately as possible. Also a new strategy for shrinkage stage borrowed from differential evolution (DE) is incorporated in the NM variants. To examine the efficiency of proposed algorithms, those are evaluated by 25 benchmark functions designed for the special session on real-parameter optimization of CEC2005. A comparison study between the hybrid algorithms and some DE algorithms and non-parametric analysis of obtained results demonstrate that the proposed algorithms outperform most of other algorithms and their difference in most cases is statistically considerable. In a later part of the comparative experiments, a comparison of the proposed algorithms with some other evolutionary algorithms reported in the CEC2005 confirms a better performance of our proposed algorithms.     

Improved biogeography-based optimization with random ring topology and Powell's method

Biogeography-based optimization (BBO) is a competitive population optimization algorithm based on biogeography theory with inherently insufficient exploration capability and slow convergence speed. To overcome limitations, we propose an improved variant of BBO, named PRBBO, for solving global optimization problems. In PRBBO, a hybrid migration operator with random ring topology, a modified mutation operator, and a self-adaptive Powell's method are rational integrated together. The hybrid migration operator with random ring topology, denoted as RMO, is created by using local ring topology to replace global topology, which can avoid the asymmetrical migration operation and enhance potential population diversity. The self-adaptive Powell's method is amended by using self-adaptive parameters for suiting evolution process to enhance solution precision quickly. Extensive experimental tests are carried out on 24 benchmark functions to show effectiveness of the proposed algorithm. Simulation results were compared with original BBO, ABC, DE, other variants of the BBO, and other state-of-the-art evolutionary algorithms. Finally, the effectiveness of operators on the performance of PRBBO is also discussed.     

A perturb biogeography based optimization with mutation for global numerical optimization

Biogeography based optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. We propose three extensions to BBO. First, we propose a new migration operation based sinusoidal migration model called perturb migration, which is a generalization of the standard BBO migration operator. Then, the Gaussian mutation operator is integrated into perturb biogeography based optimization (PBBO) to enhance its exploration ability and to improve the diversity of population. Experiments have been conducted on 23 benchmark problems of a wide range of dimensions and diverse complexities. Simulation results and comparisons demonstrate the proposed PBBO algorithm using sinusoidal migration model is better, or at least comparable to, the RCBBO based linear model, RCBBO-G, RCBBO-L and evolutionary algorithms from literature when considering the quality of the solutions obtained.     

基于正弦控制因子的Lateral变异鲨鱼优化算法

针对传统鲨鱼优化算法在求解高维目标函数时,易早熟收敛,陷入局部最优的缺陷.提出一种基于正弦控制因子的Lateral变异鲨鱼优化算法.通过正弦曲线的特性和自适应惯性权重,改善了传统鲨鱼优化算法中由于随机选取控制因子数值大小可能导致算法在迭代后期全局搜索能力降低的问题,提高了算法在迭代后期的全局收敛能力,并对最佳鲨鱼位置引入Lateral变异策略,加强了算法跳出局部最优的可能性.改进后的算法对多个shifted单峰,多峰以及固定维测试函数进行求解,实验结果表明,对比多种不同优化算法而言,本文所提LSSO算法具有更高的收敛精度和搜索速度.     

A hybrid immune multiobjective optimization algorithm

In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently.     

Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms

The mutation operation is critical to the success of genetic algorithms since it diversifies the search directions and avoids convergence to local optima. The earliest genetic algorithms use only one mutation operator in producing the next generation. Each problem, even each stage of the genetic process in a single problem, may require appropriately different mutation operators for best results. Determining which mutation operators should be used is quite difficult and is usually learned through experience or by trial-and-error. This paper proposes a new genetic algorithm, the dynamic mutation genetic algorithm, to resolve these difficulties. The dynamic mutation genetic algorithm simultaneously uses several mutation operators in producing the next generation. The mutation ratio of each operator changes according to evaluation results from the respective offspring it produces. Thus, the appropriate mutation operators can be expected to have increasingly greater effects on the genetic process. Experiments are reported that show the proposed algorithm performs better than most genetic algorithms with single mutation operators.     

Multi-strategy boosted mutative whale-inspired optimization approaches

This paper presents an improved Whale Optimization Algorithm (WOA) for global optimization. WOA is a recently introduced meta-heuristic algorithm mimicking the hunting behavior of humpback whales. Owing to its simplicity in exploratory and exploitative operators and the satisfactory efficacy, this algorithm has found its place among the well-established population-based approach utilized in many engineering and science areas. However, this method is easy to fall into local optimum when dealing with some optimization cases. In order to further enhance its exploratory and exploitative performance, three strategies are incorporated into the original method to keep a better balance between exploitation and exploration tendencies. First, the chaotic initialization phase is introduced into the optimizer to initiate the swarm of chaos-triggered whales. Then, Gaussian mutation is employed to intensify the diversity level of the evolving population. At last, a chaotic local search with a ‘shrinking’ strategy is used to enhance the exploitative leanings of the basic optimizer. In order to verify the effectiveness of the improved WOA, it is compared to four meta-heuristic and state-of-the-art evolutionary algorithms on representative benchmark functions. Trial results and simulations reveal that not only the proposed improved WOA is significantly better than those basic algorithms including original WOA but also it is superior to compared state-of-the-art approaches. Moreover, the proposed algorithm is successfully applied to realize three constrained engineering test cases, which the results suggest that the improved WOA can effectively deal with the constrained functions as well.     

蚁群遗传混合算法

将蚁群遗传混合算法分别求解离散空间的和连续空间优化问题.求解旅行商问题的混合算法是以遗传算法为整个算法的框架,利用了蚁群算法中的信息素特性的进行交叉操作;根据旅行商问题的特点,给出了4种变异策略;针对遗传算法存在的过早收敛问题,加入2-0pt方法对问题求解进行了局部优化.与模拟退火算法、标准遗传算法和标准蚁群算法进行比较,4种混合算法效果都比较好,策略D的混合算法效果最好.求解连续空间优化问题是以蚁群算法为整个算法的框架,加入遗传算法的交叉操作和变异操作,用测试函数验证了混合蚁群算法的正确性.     

基于改进粒子群算法V型非传统布局仓库通道优化设计

V型仓储布局是一种典型的非传统布局方式,针对V型布局主通道设计的问题,将主通道抽象为若干个点连接而成的折线通道,每条拣货通道按物动量大小对仓库进行分区,采用更加符合实际的存取货物作业的概率不相等的非完全随机存储策略,建立最小化平均拣货距离的仓库主通道设计数学优化模型。其次,设计了基于极值扰动算子的改进粒子群优化算法(EDO-PSO)进行算法求解,利用极值扰动算子解决易陷入局部最优问题,采用并行深度搜索策略,提高算法性能,并用Benchmark函数与其他改进PSO算法对比验证算法性能。最后,结合具体实验数据仿真分析,计算结果表明,该方法在相同货位分配策略下,能有效缩短总拣货距离,验证了方法的有效性。     

An effective krill herd algorithm with migration operator in biogeography-based optimization

Krill herd (KH) is a novel search heuristic method. To improve its performance, a biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks. The improvement involves introducing a new krill migration (KM) operator when the krill updating to deal with optimization problems more efficiently. The KM operator emphasizes the exploitation and lets the krill cluster around the best solutions at the later run phase of the search. The effects of these enhancements are tested by various well-defined benchmark functions. Based on the experimental results, this novel BBKH approach performs better than the basic KH and other optimization algorithms.     

A genetic algorithm with local search strategy for improved detection of community structure

On the basis of modularity optimization, a genetic algorithm is proposed to detect community structure in networks by defining a local search operator. The local search operator emphasizes two features: one is that the connected nodes in a network should be located in the same community, while the other is “local selection” inspired by the mechanisms of efficient message delivery underlying the small‐world phenomenon. The results of community detection for some classic networks, such as Ucinet and Pajek networks, indicate that our algorithm achieves better community structure than other methodologies based on modularity optimization, such as the algorithms based on betweenness analysis, simulated annealing, or Tasgin and Bingol's genetic algorithm. © 2009 Wiley Periodicals, Inc. Complexity, 2010     

An improved whale optimization algorithm with armed force program and strategic adjustment

In this paper, we propose a new whale army optimization algorithm with a view to solving multifarious optimization problems. The key novelty of our approach is to modify the original whale optimization algorithm to make it effective to solve the complicated, large-scale and constrained optimization problems. Our modifications mainly embody two aspects: the beneficial strategic adjustment to set key parameters and to help establish base principles in the original optimizer and the introduction of armed force program which classifies the search whales into different categories to achieve efficient cooperation. We evaluate the performance of the proposed algorithm, using three simple benchmark test functions over thirty CEC-2014 real-parameter numerical optimization problems and three constraint engineering design problems. The test results indicate that this algorithm can provide a faster local convergence rate, a higher convergence accuracy, and a lower computational complexity in comparison to traditional whale optimization algorithms and other sophisticated state of the art whale optimizers. Performance wise, it also surpasses many advanced methods for large-scaled complex functions. Furthermore, in this paper we propose a variant of whale army optimization algorithm to specifically address and solve optimizing constrained problems with a high degree of precision.     

求解柔性作业车间调度的动态群智能优化算法

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.     

Harris Hawks optimization with information exchange

The basic Harris Hawks optimization algorithm cannot take full advantage of the information sharing capability of the Harris Hawks while cooperatively searching for prey, and it is difficult to balance the exploration and development capacities of this algorithm. These factors limit the Harris Hawks optimization algorithm, such as in terms of premature convergence and ease of falling into a local optimum. To this end, an improved Harris Hawks optimization algorithm based on information exchange is proposed to optimize the continuous function and its application to engineering problems. First, an individual Harris Hawk obtains information from the shared area of cooperative foraging and the location area of collaborators, thereby realizing information exchange and sharing. Second, a nonlinear escaping energy factor with chaos disturbance is designed to better balance the local searching and the global searching of the algorithm. Finally, a numerical experiment is conducted with four benchmark test functions and five CEC-2017 real-parameter numerical optimization problems as well as seven practical engineering problems. The results show that the proposed algorithm outperforms the basic Harris Hawks optimization algorithm and other intelligence optimization algorithms in terms of the convergence rate, solution accuracy, and robustness.     

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

Dynamic optimization and multi-objective optimization have separately gained increasing attention from the research community during the last decade. However, few studies have been reported on dynamic multi-objective optimization (dMO) and scarce effective dMO methods have been proposed. In this paper, we fulfill these gabs by developing new dMO test problems and new effective dMO algorithm. In the newly designed dMO problems, Pareto-optimal decision values (i.e., Pareto-optimal solutions: POS) or both POS and Pareto-optimal objective values (i.e., Pareto-optimal front: POF) change with time. A new multi-strategy ensemble multi-objective evolutionary algorithm (MS-MOEA) is proposed to tackle the challenges of dMO. In MS-MOEA, the convergence speed is accelerated by the new offspring creating mechanism powered by adaptive genetic and differential operators (GDM); a Gaussian mutation operator is employed to cope with premature convergence; a memory like strategy is proposed to achieve better starting population when a change takes place. In order to show the advantages of the proposed algorithm, we experimentally compare MS-MOEA with several algorithms equipped with traditional restart strategy. It is suggested that such a multi-strategy ensemble approach is promising for dealing with dMO problems.     

基于动态自适应t分布变异的人群搜索算法

针对人群搜索算法在进化后期大量个体聚集局部最优时,易陷入局部最优,搜索精度低的缺陷,提出一种基于t分布变异的人群搜索算法.算法使用动态自适应方式确定变异步长,引入t分布变异算子以融合柯西变异和高斯变异的优点,促进算法在进化早期具备良好的全局探索能力,在进化后期收获较强的局部开发能力,增加种群的多样性;采用边界缓冲墙策略处理越界问题,避免越界个体聚集在边界值上的缺陷.实验结果表明,算法比基本人群搜索算法具有更高的寻优精度和收敛速度,是一种有效的算法.     

Some Variants of the Controlled Random Search Algorithm for Global Optimization

We suggested some modifications to the controlled random search (CRS) algorithm for global optimization. We introduce new trial point generation schemes in CRS, in particular, point generation schemes using linear interpolation and mutation. Central to our modifications is the probabilistic adaptation of point generation schemes within the CRS algorithm.A numerical study is carried out using a set of 50 test problems many of which are inspired by practical applications. Numerical experiments indicate that the resulting algorithms are considerably better than the previous versions. Thus, they offer a reasonable alternative to many currently available stochastic algorithms, especially for problems requiring direct search type methods.     

基于统计引导和多项式差分学习的樽海鞘优化算法

樽海鞘优化算法相较于传统的群体智能优化算法,具有较好的鲁棒性和寻优能力。但仍存在全局寻优能力有限、执行效率不够高、易陷入局部极值的缺陷。针对上述问题,本文提出一种新的多项式差分学习策略,以区分和改进传统的线性差分方法;并设计一种随机种群划分方式,使得信息可以在邻域拓扑内均匀传递;另外,本文定义多项式差分学习的全局探索算子和局部开发算子,引入统计引导系数A,开启不同的多项式学习方法,从而进一步提高算法的全局搜索能力和寻优精度。最后,本文通过标准测试函数和实际应用问题的对比检验,证实了改进算法的优越性和鲁棒性,拓展和丰富了原算法的应用范围。