GSA: A Gravitational Search Algorithm

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.     

Honey Bees Mating Optimization algorithm for financial classification problems

Nature inspired methods are approaches that are used in various fields and for the solution for a number of problems. This study uses a nature inspired method, namely Honey Bees Mating Optimization, that is based on the mating behaviour of honey bees for a financial classification problem. Financial decisions are often based on classification models which are used to assign a set of observations into predefined groups. One important step towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. The proposed method uses for the feature selection step, the Honey Bees Mating Optimization algorithm while for the classification step, Nearest Neighbor based classifiers are used. The performance of the method is tested in a financial classification task involving credit risk assessment. The results of the proposed method are compared with the results of a particle swarm optimization algorithm, an ant colony optimization, a genetic algorithm and a tabu search algorithm.     

Block-matching algorithm based on harmony search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to save computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The proposed method has been compared to other BM algorithms in terms of velocity and coding quality. Experimental results demonstrate that the proposed algorithm exhibits the best balance between coding efficiency and computational complexity.     

一种运动搜索算法的优化模型设计*

为了减少运动估计中全搜索过程的运算量,提出了一种运动搜索算法的优化模型。通过对运动搜索区域和搜索顺序的优化,缩小了全搜索范围并提高了匹配块搜索的速度。实验结果表明,在图像质量得到优化的前提下,较全搜索算法,该模型在不同频率的视频序列中搜索速度有了大幅的提高。     

具有混合群智能行为的萤火虫群优化算法研究

萤火虫群优化算法是一种新型的群智能优化算法,基本的萤火虫群优化算法存在收敛精度低等问题。为了提高算法的性能,借鉴蜂群和鸟群的群体智能行为,改进萤火虫群优化算法的移动策略。运用均匀设计调整改进算法的参数取值。若干经典测试问题的实验仿真结果表明,引入混合智能行为大幅提升了算法的优化性能。     

新型飞蛾火焰优化算法的研究

飞蛾火焰优化算法（Moth-Flame Optimization，MFO）是一种自然激励且易于实现的全局优化算法，在许多实际优化任务中表现出良好的性能。然而，MFO算法存在早熟收敛和容易陷入局部最优解的问题，针对这些不足，提出了一种Kent混沌动态惯性权值的改善飞蛾火焰优化算法（Ameliorative MFO，AMFO）。在AMFO算法中，引入Kent混沌映射搜索策略帮助当前最优解跳出局部最优；采用基于适应度值和迭代次数的动态惯性权值策略来平衡算法的开发和探索能力，以进一步提升MFO算法性能。在8个经典benchmark函数上验证AMFO算法的搜索精度和性能，并将其结果与标准飞蛾火焰优化算法、粒子群算法和差分进化算法进行比较，仿真结果表明AMFO算法具有较好的搜索性能。     

Multilevel minimum cross entropy threshold selection based on the firefly algorithm

The minimum cross entropy thresholding (MCET) has been widely applied in image thresholding. The search mechanism of firefly algorithm inspired by the social behavior of the swarms of firefly and the phenomenon of bioluminescent communication, is used to search for multilevel thresholds for image segmentation in this paper. This new multilevel thresholding algorithm is called the firefly-based minimum cross entropy thresholding (FF-based MCET) algorithm. Four different methods that are the exhaustive search, the particle swarm optimization (PSO), the quantum particle swarm optimization (QPSO) and honey bee mating optimization (HBMO) methods are implemented for comparison with the results of the proposed method. The experimental results show that the proposed FF-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method when the number of thresholds is less than 5. The need of computation time of using the FF-based MCET algorithm is the least, meanwhile, the results using the FF-based MCET algorithm is superior to the ones of PSO-based and QPSO-based MCET algorithms but is not significantly different to the HBMO-based MCET algorithm.     

Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion

This paper proposes a new global optimization metaheuristic called Galactic Swarm Optimization (GSO) inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. GSO employs multiple cycles of exploration and exploitation phases to strike an optimal trade-off between exploration of new solutions and exploitation of existing solutions. In the explorative phase different subpopulations independently explore the search space and in the exploitative phase the best solutions of different subpopulations are considered as a superswarm and moved towards the best solutions found by the superswarm. In this paper subpopulations as well as the superswarm are updated using the PSO algorithm. However, the GSO approach is quite general and any population based optimization algorithm can be used instead of the PSO algorithm. Statistical test results indicate that the GSO algorithm proposed in this paper significantly outperforms 4 state-of-the-art PSO algorithms and 4 multiswarm PSO algorithms on an overwhelming majority of 15 benchmark optimization problems over 50 independent trials and up to 50 dimensions. Extensive simulation results show that the GSO algorithm proposed in this paper converges faster to a significantly more accurate solution on a wide variety of high dimensional and multimodal benchmark optimization problems.     

粒子群分形进化算法

在传统粒子群优化(PSO)算法的基础上,提出粒子群分形进化算法(FEPSO).FEPSO利用分形布朗运动模型中的无规则运动特性模拟优化目标函数未知特性,隐含的趋势变化模拟优化目标函数极值变化的总趋势,从而克服个体过于随机进化和早熟的现象.与传统的PSO算法相比,文中算法中每个粒子包含分形进化阶段.在分形进化阶段,粒子在解的子空间以不同的分形参数进行分形布朗运动方式搜索解空间,并对其分量进行更新.仿真实验结果表明,该算法对大部分标准复合测试函数都具有较强的全局搜索能力,其性能超过国际上最近提出的基于PSO的改进算法.     

基于模拟退火的自适应水波优化算法

水波优化算法(Water Wave Optimization,WWO)是一种基于浅水波理论的新兴智能优化算法。在简化水波优化算法(Simplified Water Wave Optimization,SimWWO)的基础上,提出水波优化算法的一个改进版本。针对WWO算法在寻优过程中未能有效利用水波历史状态和经验的问题,提出一种自适应的参数调整策略:根据水波进化过程中的性能改善指标自适应调整算法的波长系数,提高搜索效率;同时,针对算法后期容易陷入局部最优的情况,加入模拟退火的思想,以一定的概率接受劣质解,避免算法陷入局部最优。通过以上两个操作可以更好地平衡全局搜索和局部搜索。在CEC 2015函数测试集上进行比较,结果证明改进后的算法有效地提高了综合性能。     

A new modification approach on bat algorithm for solving optimization problems

Optimization can be defined as an effort of generating solutions to a problem under bounded circumstances. Optimization methods have arisen from a desire to utilize existing resources in the best possible way. An important class of optimization methods is heuristic algorithms. Heuristic algorithms have generally been proposed by inspiration from the nature. For instance, Particle Swarm Optimization has been inspired by social behavior patterns of fish schooling or bird flocking. Bat algorithm is a heuristic algorithm proposed by Yang in 2010 and has been inspired by a property, named as echolocation, which guides the bats’ movements during their flight and hunting even in complete darkness. In this work, local and global search characteristics of bat algorithm have been enhanced through three different methods. To validate the performance of the Enhanced Bat Algorithm (EBA), standard test functions and constrained real-world problems have been employed. The results obtained by these test sets have proven EBA superior to the standard one. Furthermore, the method proposed in this study is compared with recently published studies in the literature on real-world problems and it is proven that this method is more effective than the studies belonging to other literature on this sort of problems.     

AVS分像素运动估计优化算法

针对AVS分像素运动估计的问题,提出一种优化的估计方法。包括分像素自适应提前中止算法和改进的分像素内插方法。该方法能降低分像素运动搜索的内插复杂度和搜索次数,适用于DSP、FPGA等嵌入式平台的实现。实验结果表明,在保证图像质量的前提下,该算法的总体运算复杂度降低了70%以上。     

Human mental search-based multilevel thresholding for image segmentation

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.     

多分支混沌变异的头脑风暴优化算法

头脑风暴优化算法是一种受人类群体行为启发的新型群智能优化算法。该算法通过模拟人类使用头脑风暴创造性解决问题的行为,在解空间中分析个体分布,并使用变异生成新个体,多次迭代求得最优解,具有较高的鲁棒性和自适应能力。针对头脑风暴优化算法精度较差、易陷入局部最优导致早熟收敛的缺陷,提出了一种多分支混沌变异的头脑风暴优化算法。该算法选取8种混沌映射,设计了一种多分支混沌变异算子。当原始算法陷入局部最优时,使用多分支混沌变异生成新个体,利用多种混沌运动的遍历性、随机性和多样性,扩大了混沌空间的范围,增强了算法全局搜索的能力。对10个经典测试函数的10、20、30维问题进行测试,并与原始头脑风暴优化算法、粒子群优化算法、遗传算法和布谷鸟搜索算法进行对比,实验结果表明,所提出的算法可以有效避免陷入局部最优,具有更高的稳定性和全局搜索能力。     

基于蚁群算法的多目标网页综合评价策略

蚁群算法是Marco和Dorigo等学者在真实蚂蚁觅食行为的启发下提出的一种群智能优化算法。为了提高搜索引擎系统中的查全率和查准率,采用理论分析和实验相结合的方式,研究了蚁群算法在搜索引擎系统中的应用。引用蚁群算法量化用户偏爱度,提出了一种基于网页的链接结构、内容关联度和用户偏爱度三个指标的多目标优化模型的网页价值综合评价体系。从理论上阐述了蚁群算法应用于搜索引擎系统的可行性及适应性。最后实验仿真证明了该网页价值综合评价策略的有效性和优越性。     

基于正交梯度搜索的动态系统递阶优化辨识

提出了一种辨识线性时不变状态空间系统参数的正交梯度二步递阶优化方法. 通过极小化输出误差目标函数获得了系统参数估计; 提出了正交梯度搜索方法用于解决系统参数的非唯一性问题, 正交梯度搜索的本质是在输入-输出等价类相切平面的正交垂空间更新系统参数; 给出了用 L-M 算法进行参数优化的充分条件; 提出的系统参数递阶优化辨识方法包括两步: 首先用给出的自适应 L-M 算子正交梯度方法确定参数优化方向; 其次由一维搜索方法计算最佳步长. 蒙特卡罗数值仿真实验表明本文提出的方法具有收敛速度快、抗噪能力强以及数值稳定性好等优点.     

基于改进鲸鱼优化算法的多无人机围捕

无人机围捕是一项具有挑战性和现实意义的任务,为使无人机可以成功有效地围捕移动目标,提出一种基于动态预测围捕点和改进鲸鱼优化算法的多无人机围捕算法。在环境未知,目标运动轨迹未知的情况下,首先利用多项式拟合预测目标运动轨迹,通过动态预测步数得到预测点,在其周围设置围捕点,然后使用双向协商法为无人机合理分配各个目标点。针对鲸鱼优化算法容易陷入局部最优的缺点,提出基于自适应权重和改变螺旋线位置更新的方法,从而提升算法的开发能力和搜索能力。最终在不同实验环境下进行多次实验仿真,实验结果表明了所提出算法的有效性。     

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.     

A hybrid particle swarm optimization algorithm for the vehicle routing problem

This paper introduces a new hybrid algorithmic nature inspired approach based on particle swarm optimization, for successfully solving one of the most popular supply chain management problems, the vehicle routing problem. The vehicle routing problem is considered one of the most well studied problems in operations research. The proposed algorithm for the solution of the vehicle routing problem, the hybrid particle swarm optimization (HybPSO), combines a particle swarm optimization (PSO) algorithm, the multiple phase neighborhood search–greedy randomized adaptive search procedure (MPNS–GRASP) algorithm, the expanding neighborhood search (ENS) strategy and a path relinking (PR) strategy. The algorithm is suitable for solving very large-scale vehicle routing problems as well as other, more difficult combinatorial optimization problems, within short computational time. It is tested on a set of benchmark instances and produced very satisfactory results. The algorithm is ranked in the fifth place among the 39 most known and effective algorithms in the literature and in the first place among all nature inspired methods that have ever been used for this set of instances.     

Immigrant Population Search Algorithm for Solving Constrained Optimization Problems

This article introduces the Immigrant Population Search Algorithm (IPSA) inspired by the pattern of human population migration to find better habitats. The algorithm is viewed as a new optimization method for solving constrained optimization problems, and it belongs to the set of population-based algorithms that are proposed for combinatorial optimization. In this algorithm, the life environment is the solution space of the problem. Every point of this space is a solution for the problem, which may be feasible or infeasible, and the quality of life at that point is the value of fitness function for that solution. Each population group tries to investigate feasible and better habitats. In other words, it tries to optimize the problem. After the algorithm steps are described, the efficiency of the algorithm is compared to that of three other metaheuristic algorithms that are used to optimize some mathematic problems. The results show that the proposed algorithm performs better than the other three methods.