Binary optimization using hybrid particle swarm optimization and gravitational search algorithm

The PSOGSA is a novel hybrid optimization algorithm, combining strengths of both particle swarm optimization (PSO) and gravitational search algorithm (GSA). It has been proven that this algorithm outperforms both PSO and GSA in terms of improved exploration and exploitation. The original version of this algorithm is well suited for problems with continuous search space. Some problems, however, have binary parameters. This paper proposes a binary version of hybrid PSOGSA called BPSOGSA to solve these kinds of optimization problems. The paper also considers integration of adaptive values to further balance exploration and exploitation of BPSOGSA. In order to evaluate the efficiencies of the proposed binary algorithm, 22 benchmark functions are employed and divided into three groups: unimodal, multimodal, and composite. The experimental results confirm better performance of BPSOGSA compared with binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), and genetic algorithm in terms of avoiding local minima and convergence rate.     

An experimental study of adaptive control for evolutionary algorithms

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.     

Memetic search in artificial bee colony algorithm

Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over benchmark as well as real world optimization problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is a enough chance to skip the true solution due to large step size. In order to balance between diversity and convergence capability of the ABC, a new local search phase is integrated with the basic ABC to exploit the search space identified by the best individual in the swarm. In the proposed phase, ABC works as a local search algorithm in which, the step size that is required to update the best solution, is controlled by Golden Section Search approach. The proposed strategy is named as Memetic ABC (MeABC). In MeABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. MeABC is established as a modified ABC algorithm through experiments over 20 test problems of different complexities and 4 well known engineering optimization problems.     

一种自适应鲸鱼快速优化算法

针对标准鲸鱼优化算法存在的局部搜索能力不足、收敛速度慢等问题,提出了一种自适应鲸鱼快速优化算法AWOA。该算法根据个体的集散程度自适应选择全局搜索或局部搜索,在两者之间实现了动态平衡。针对偏离样本平均位置程度较高的个体引入Levy Flight进行二次优化,进一步扩大搜索区域,保证了算法的全局搜索能力。采用标准测试函数证实了AOWA具有较高的收敛速度及稳定性。将AWOA应用于无人车路径规划问题,仿真结果表明其具有稳定的局部搜索能力和全局搜索能力。     

An Artificial Bee Colony algorithm with guide of global & local optima and asynchronous scaling factors for numerical optimization

Artificial Bee Colony (ABC) algorithm is a wildly used optimization algorithm. However, ABC is excellent in exploration but poor in exploitation. To improve the convergence performance of ABC and establish a better searching mechanism for the global optimum, an improved ABC algorithm is proposed in this paper. Firstly, the proposed algorithm integrates the information of previous best solution into the search equation for employed bees and global best solution into the update equation for onlooker bees to improve the exploitation. Secondly, for a better balance between the exploration and exploitation of search, an S-type adaptive scaling factors are introduced in employed bees’ search equation. Furthermore, the searching policy of scout bees is modified. The scout bees need update food source in each cycle in order to increase diversity and stochasticity of the bees and mitigate stagnation problem. Finally, the improved algorithms is compared with other two improved ABCs and three recent algorithms on a set of classical benchmark functions. The experimental results show that the our proposed algorithm is effective and robust and outperform than other algorithms.     

基于搜索能力均衡的人工蜂群算法

鉴于标准人工蜂群算法（ABC）局部开发能力不足,提出一种改进搜索策略的人工蜂群算法（IABC）。为提高ABC的局部开发能力,在其雇佣蜂阶段引入了一个新的具有最好个体引导的解搜索方程,为均衡ABC的搜索能力,在ABC跟随蜂阶段的搜索策略中引入了新的随机因素以增强ABC的全局探索能力,为了进一步平衡全局探索和局部开发能力,改进了ABC的侦察蜂搜索机制。为验证IABC的收敛效果,通过在12个复杂基准测试函数上的仿真实验并与其他算法相比较,发现IABC的收敛性能有显著提高。     

Particle swarm optimization with adaptive time-varying topology connectivity

Particle swarm optimization (PSO) has shown its competitive performance for solving benchmark and real-world optimization problems. Nevertheless, it requires better control of exploration/exploitation searches to prevent the premature convergence of swarms. Thus, this paper proposes a new PSO variant called PSO with adaptive time-varying topology connectivity (PSO-ATVTC) that employs an ATVTC module and a new learning framework. The proposed ATVTC module specifically aims to balance the algorithm's exploration/exploitation searches by varying the particle's topology connectivity with time according to its searching performance. The proposed learning framework consists of a new velocity update mechanism and a new neighborhood search operator to improve the algorithm's performance. A comprehensive study was conducted on 24 benchmark functions and one real-world problem. Compared with nine well-established PSO variants and six other cutting-edge metaheuristic search algorithms, the searching performance of PSO-ATVTC was proven to be more prominent in majority of the tested problems.     

A new Q-learning algorithm based on the metropolis criterion

The balance between exploration and exploitation is one of the key problems of action selection in Q-learning. Pure exploitation causes the agent to reach the locally optimal policies quickly, whereas excessive exploration degrades the performance of the Q-learning algorithm even if it may accelerate the learning process and allow avoiding the locally optimal policies. In this paper, finding the optimum policy in Q-learning is described as search for the optimum solution in combinatorial optimization. The Metropolis criterion of simulated annealing algorithm is introduced in order to balance exploration and exploitation of Q-learning, and the modified Q-learning algorithm based on this criterion, SA-Q-learning, is presented. Experiments show that SA-Q-learning converges more quickly than Q-learning or Boltzmann exploration, and that the search does not suffer of performance degradation due to excessive exploration.     

An adaptive artificial bee colony algorithm based on objective function value information

Artificial bee colony (ABC) algorithm is a novel biological-inspired optimization algorithm, which has many advantages compared with other optimization algorithm, such as less control parameters, great global optimization ability and easy to carry out. It has proven to be more effective than some evolutionary algorithms (EAs), particle swarm optimization (PSO) and differential evolution (DE) when testing on both benchmark functions and real issues. ABC, however, its solution search equation is poor at exploitation. For overcoming this insufficiency, two new solution search equations are proposed in this paper. They apply random solutions to take the place of the current solution as base vector in order to get more useful information. Exploitation is further improved on the basis of enhancing exploration by utilizing the information of the current best solution. In addition, the information of objective function value is introduced, which makes it possible to adjust the step-size adaptively. Owing to their respective characteristics, the new solution search equations are combined to construct an adaptive algorithm called MTABC. The methods our proposed balance the exploration and exploitation of ABC without forcing severe extra overhead in respect of function evaluations. The performance of the MTABC algorithm is extensively judged on a set of 20 basic functions and a set of 10 shifted or rotated functions, and is compared favorably with other improved ABCs and several state-of-the-art algorithms. The experimental results show that the proposed algorithm has a higher convergence speed and better search ability for almost all functions.     

融合动态反向学习的阿奎拉鹰与哈里斯鹰混合优化算法

阿奎拉鹰优化算法(Aquila optimizer, AO)和哈里斯鹰优化算法(Harris hawks optimization, HHO)是近年提出的优化算法。AO算法全局寻优能力强,但收敛精度低,容易陷入局部最优,而HHO算法具有较强的局部开发能力,但存在全局探索能力弱,收敛速度慢的缺陷。针对原始算法存在的局限性,本文将两种算法混合并引入动态反向学习策略,提出一种融合动态反向学习的阿奎拉鹰与哈里斯鹰混合优化算法。首先,在初始化阶段引入动态反向学习策略提升混合算法初始化性能与收敛速度。此外,混合算法分别保留了AO的探索机制与HHO的开发机制,提高算法的寻优能力。仿真实验采用23个基准测试函数和2个工程设计问题测试混合算法优化性能,并对比了几种经典反向学习策略,结果表明引入动态反向学习的混合算法收敛性能更佳,能够有效求解工程设计问题。     

一种基于种群熵的自适应粒子群算法

提出了一种基于种群熵的自适应粒子群算法,采用2个基准函数对新算法进行了测试.测试结果表明,新算法有效地均衡了算法的探测和开采能力,在解决复杂多峰函数优化问题时,与基本粒子群算法相比,具有更强的摆脱局部极值点的能力,且执行效率降低不多.     

Artificial Bee Colony Algorithm Based on Adaptive Search Equation and Extended Memory

From the perspective of psychology, a modified artificial bee colony algorithm (ABC, for short) based on adaptive search equation and extended memory (ABCEM, for short) for global optimization is proposed in this paper. In the proposed ABCEM algorithm, an extended memory factor is introduced into store employed bees’ and onlooker bees’ historical information comprising recent food sources, personal best food sources, and global best food sources, and the solution search equation for the employed bees is equipped with adaptive ability. Moreover, a parameter is employed to describe the importance of the extended memory. Furthermore, the extended memory is added to two solution search equations for the employed bees and the onlookers to improve the quality of food source. To evaluate the proposed algorithm, experiments are conducted on a set of numerical benchmark functions. The results show that the proposed algorithm can balance the exploration and exploitation, and can improve the accuracy of optima solutions and convergence speed compared with other current improved ABCs for global optimization in most of the tested functions.     

具有自适应全局最优引导快速搜索策略的人工蜂群算法

针对人工蜂群算法存在开发与探索能力不平衡的缺点,提出了具有自适应全局最优引导快速搜索策略的改进算法.在该策略中,首先采蜜蜂利用自适应搜索方程平衡了不同搜索方法的探索和开发能力;其次跟随蜂利用全局最优引导邻域搜索方程对蜜源进行精细化搜索,以提高其收敛精度和全局搜索能力.14个标准测试函数的仿真结果表明,相比其他算法,所提出的改进算法有效平衡了算法的开发与探索能力,并提高了其最优解的精度及收敛速度.     

基于分解优化的多星合成观测调度算法

某些卫星的侧摆性能较差, 必须进行合成观测以提高观测效率. 研究了多星联合对地观测中的任务合成观测调度问题. 提出了将原问题分解为任务分配与任务合成的分解优化思路. 任务分配为任务选择卫星资源及时间窗口; 任务合成则针对该分配方案,将分配到各卫星的任务按照轨道圈次分组, 分别进行最优合成. 采用蚁群优化算法(Ant colony optimization, ACO)求解任务分配问题, 通过自适应参数调整及信息素平滑策略, 实现全局搜索和快速收敛间的平衡.提出了基于动态规划的最优合成算法, 求解任务合成子问题,能够在多项式时间内求得最优合成方案. 依据分配方案的合成结果, 得到优化方案的特征信息, 反馈并引导蚁群优化算法对任务分配方案的搜索过程. 大规模测试算例验证了本文算法的效率.     

一种自适应柯西变异的反向学习粒子群优化算法

针对传统粒子群优化算法易出现早熟的问题,提出了一种自适应变异的反向学习粒子群优化算法。该算法在一般性反向学习方法的基础上,提出了自适应柯西变异策略(ACM)。采用一般性反向学习策略生成反向解,可扩大搜索空间,增强算法的全局勘探能力。为避免粒子陷入局部最优解而导致搜索停滞现象的发生,采用ACM策略对当前最优粒子进行扰动,自适应地获取变异点,在有效提高算法局部开采能力的同时,使算法能更加平稳快速地收敛到全局最优解。为进一步平衡算法的全局搜索与局部探测能力,采用非线性的自适应惯性权值。将算法在14个测试函数上与多种基于反向学习策略的PSO算法进行对比,实验结果表明提出的算法在解的精度以及收敛速度上得到了大幅度的提高。     

基于混合策略改进的鲸鱼优化算法

针对传统鲸鱼优化算法收敛速度慢、易陷入局部最优等问题,提出一种基于混合策略改进的鲸鱼优化算法。首先,引入非线性调整策略改进收敛因子,平衡算法的全局探索与局部开发能力并加快算法收敛速度;然后,将自适应权重系数引入鲸鱼位置更新式中,从而提高算法的寻优精度;最后,结合人工蜂群算法的limit阈值思想,使算法能够有效跳出局部最优,改善算法早熟收敛现象。通过对14个基准测试函数在不同维度上的仿真实验表明,改进算法具有较高的寻优精度和较快的收敛速度。     

具有自适应搜索策略的混合人工蜂群算法

基本人工蜂群算法及其搜索策略侧重探索,为增强算法的开发能力,提出一种具有自适应搜索策略的混合人工蜂群算法。将目标函数值信息和最优解引导信息引入搜索策略,提出具有自适应机制、开发能力强的搜索策略;为防止“早熟”现象,利用三个不同随机食物源和高斯分布,设计出全局探索能力较强的搜索策略。将两个搜索策略在雇佣蜂阶段混合以平衡算法的探索与开发能力,在观察蜂阶段使用具有自适应机制、开发能力强的搜索策略以加快收敛。与基本及具有代表性的改进人工蜂群算法在20个标准测试函数中进行对比实验,结果表明所提算法具有更好的搜索能力和更快的收敛速度。     

基于局部并行搜索的分布式约束优化算法框架

针对当前局部搜索算法在求解大规模、高密度的分布式约束优化问题（DCOP）时,求解困难且难以跳出局部最优取得进一步优化等问题,提出一种基于局部并行搜索的分布式约束优化算法框架（LPOS）,算法中agent通过自身的取值并行地搜索局部所有邻居取值来进一步扩大对解空间的搜索,从而避免算法过早陷入局部最优。为了保证算法的收敛性与稳定性,设计了一种自适应平衡因子K来平衡算法对解的开发和继承能力,并在理论层面证明了并行搜索优化算法可以扩大对解空间的搜索,自适应平衡因子K可以实现平衡目的。综合实验结果表明,基于该算法框架的算法在求解低密度和高密度DCOP时性能都优于目前最新的算法。特别是在求解高密度DCOP中有显著的提升。     

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。     

应用反向学习和差分进化的群搜索优化算法

针对标准群搜索优化算法在解决一些复杂优化问题时容易陷入局部最优且收敛速度较慢的问题,提出一种应用反向学习和差分进化的群搜索优化算法(Group Search Optimization with Opposition-based Learning and Diffe-rential Evolution,OBDGSO)。该算法利用一般动态反向学习机制产生反向种群,扩大算法的全局勘探范围;对种群中较优解个体实施差分进化的变异操作,实现在较优解附近的局部开采,以改善算法的求解精度和收敛速度。这两种策略在GSO算法中相互协同,以更好地平衡算法的全局搜索能力和局部开采能力。将OBDGSO算法和另外4种群智能算法在12个基准测试函数上进行实验,结果表明OBDGSO算法在求解精度和收敛速度上具有较显著的性能优势。