一种新型的樽海鞘群算法及其应用

为了解决樽海鞘群算法SSA在寻优过程中存在收敛速度慢、计算精度差等问题,提出一种新型的樽海鞘群算法NSSA。 首先分析SSA中樽海鞘在追随领导者过程中的不足,然后借鉴灰狼优化算法中追随头狼的思想来改进樽海鞘追随领导者的方式。在23个基准函数上对NSSA与其他算法进行性能比较,并把该算法应用于图像匹配之中。所有实验结果表明,NSSA具有更好的收敛速度、计算精度和鲁棒性。     

HMOSHSSA: a hybrid meta-heuristic approach for solving constrained optimization problems

This paper proposes a novel hybrid multi-objective optimization algorithm named HMOSHSSA by synthesizing the strengths of Multi-objective Spotted Hyena Optimizer (MOSHO) and Salp Swarm Algorithm (SSA). HMOSHSSA utilizes the exploration capability of MOSHO to explore the search space effectively and leader and follower selection mechanism of SSA to achieve global best solution with faster convergence. The proposed algorithm is evaluated on 24 benchmark test functions, and its performance is compared with seven well-known multi-objective optimization algorithms. The experimental results demonstrate that HMOSHSSA acquires very competitive results and outperforms other algorithms in terms of convergence speed, search-ability and accuracy. Additionally, HMOSHSSA is also applied on seven well-known engineering problems to further verify its efficacy. The results reveal the effectiveness of proposed algorithm toward solving real-life multi-objective optimization problems.

     

基于混沌映射的自适应樽海鞘群算法

针对樽海鞘群算法收敛速度慢、易陷入局部最优等问题,提出了一种基于混沌映射的自适应樽海鞘群算法。在种群初始化阶段引入混沌映射来增强种群的多样性,提高算法的收敛速度;改进领导者的更新方式,同时加入自适应权重,提高算法的探索和开发能力;改进追随者的位置更新方式,减少追随者的盲目性。通过对10个测试函数进行仿真实验,并与其他优化算法进行比较,实验结果表明,在不改变原有时间复杂度的前提下,提出的算法在收敛速度和寻优精度上有较大的提升,具有更好的优化性能。     

ESSAWOA: Enhanced Whale Optimization Algorithm integrated with Salp Swarm Algorithm for global optimization

In this paper, a novel hybrid meta-heuristic algorithm called ESSAWOA is proposed for solving global optimization problems. The main idea of ESSAWOA is to enhance Whale Optimization Algorithm (WOA) by combining the mechanism of Salp Swarm Algorithm (SSA) and Lens Opposition-based Learning strategy (LOBL). The hybridization process includes three parts: First, the leader mechanism with strong exploitation of SSA is applied to update the population position before the basic WOA operation. Second, the nonlinear parameter related to the convergence property in SSA is introduced to the two phases of encircling prey and bubble-net attacking in WOA. Third, LOBL strategy is used to increase the population diversity of the proposed optimizer. The hybrid design is expected to significantly enhance the exploitation and exploration capacity of the proposed algorithm. To investigate the effectiveness of ESSAWOA, twenty-three benchmark functions of different dimensions and three classical engineering design problems are performed. Furthermore, SSA, WOA and seven other well-known meta-heuristic algorithms are employed to compare with the proposed optimizer. Our results reveal that ESSAWOA can effectively and quickly obtain the promising solution of these optimization problems in the search space. The performance of ESSAWOA is significantly superior to the basic WOA, SSA and other meta-heuristic algorithms.

     

Harmonized salp chain-built optimization

As an optimization paradigm, Salp Swarm Algorithm (SSA) outperforms various population-based optimizers in the perspective of the accuracy of obtained solutions and convergence rate. However, SSA gets stuck into sub-optimal solutions and degrades accuracy while solving the complex optimization problems. To relieve these shortcomings, a modified version of the SSA is proposed in the present work, which tries to establish a more stable equilibrium between the exploration and exploitation cores. This method utilizes two different strategies called opposition-based learning and levy-flight (LVF) search. The algorithm is named m-SSA, and its validation is performed on a well-known set of 23 classical benchmark problems. To observe the strength of the proposed method on the scalability of the test problems, the dimension of these problems is varied from 50 to 1000. Furthermore, the proposed m-SSA is also used to solve some real engineering optimization problems. The analysis of results through various statistical measures, convergence rate, and statistical analysis ensures the effectiveness of the proposed strategies integrated with the m-SSA. The comparison of the m-SSA with the conventional SSA, variants of SSA and some other state-of-the-art algorithms illustrate its enhanced search efficiency.

     

混沌精英质心拉伸机制的樽海鞘群算法

针对樽海鞘群算法求解精度不高的缺点,提出一种混沌精英质心拉伸机制的樽海鞘群算法。引入改进的Tent混沌序列生成初始种群,以增加初始个体的多样性;选择最优个体采用精英质心拉伸机制,可增强全局搜索能力。将改进算法在12个典型复杂函数和CEC2014函数优化问题上进行仿真实验,并同经典的遗传算法和粒子群算法进行对比。结果表明,混沌精英质心拉伸机制的樽海鞘群算法具有更好的全局搜索能力,寻优精度比标准算法有所增强。在求解高维和多峰测试函数上,改进算法拥有更好的性能。     

Improved salp swarm algorithm based on weight factor and adaptive mutation

Salp Swarm Algorithm (SSA) is a novel swarm intelligent algorithm with good performance. However, like other swarm-based algorithms, it has insufficiencies of low convergence precision and slow convergence speed when dealing with high-dimensional complex optimisation problems. In response to this concerning issue, in this paper, we propose an improved SSA named as WASSA. First of all, dynamic weight factor is added to the update formula of population position, aiming to balance global exploration and local exploitation. In addition, in order to avoid premature convergence and evolution stagnation, an adaptive mutation strategy is introduced during the evolution process. Disturbance to the global extremum promotes the population to jump out of local extremum and continue to search for an optimal solution. The experiments conducted on a set of 28 benchmark functions show that the improved algorithm presented in this paper displays obvious superiority in convergence performance, robustness as well as the ability to escape local optimum when compared with SSA.     

不同映射的混沌免疫进化算法性能分析

针对目前混沌免疫进化算法采用的混沌映射类型单一,并存在对混沌映射影响算法性能大小和机制缺乏深入研究等问题,分析和探讨基于不同混沌映射混沌免疫进化算法的性能。对几个典型测试函数的比较结果表明,Logistic-CIEA和Cubic-CIEA的性能易出现大波动,Kent-CIEA则具有相对稳定的收敛速度,表现出较强的鲁棒性。由此证明,混沌映射作为产生局部搜索轨迹的迭代函数,其混沌特性对算法性能影响较大。     

基于混沌麻雀搜索算法的无人机航迹规划方法

针对无人机（UAV）航迹规划求解计算量大、难收敛等问题,提出了一种基于混沌麻雀搜索算法（CSSA）的航迹规划方法。首先,建立二维任务空间模型与航迹代价模型,将航迹规划问题转化为多维函数优化问题;其次,采用立方映射初始化种群,并使用反向学习策略（OBL）引入精英粒子,增强种群多样性,扩大搜索区域范围;然后,引入正弦余弦算法（SCA）,并采用线性递减策略平衡算法的开发与探索能力,当算法陷入停滞时,采用高斯游走策略帮助算法跳出局部最优;最后,将提出的改进算法在15个基准测试函数中进行性能验证,并应用于航迹规划问题求解。仿真结果表明,CSSA的寻优性能优于粒子群优化（PSO）算法、天牛群优化（BSO）算法、鲸鱼优化算法（WOA）、灰狼优化（GWO）算法和麻雀搜索算法（SSA）,并且能够快速地得到一条代价最优、满足约束的安全可行航迹,验证了所提方法的有效性。     

基于自适应Tent混沌搜索的粒子群优化算法

为解决粒子群优化算法易于陷入局部最优问题,提出基于自适应Tent混沌搜索的粒子群优化算法。应用Tent 映射初始化均匀分布的粒群,并以当前整个粒子群迄今为止搜索到的最优位置为基础产生Tent混沌序列,混沌序列的搜索范围采用自适应调整方法。该方法可以有效避免计算的盲目性,还能够快速搜寻到最优解。实验表明该算法在多个标准测试函数下都超越了同类改进算法。     

A new chaotic multi-verse optimization algorithm for solving engineering optimization problems

Multi-verse optimization algorithm (MVO) is one of the recent meta-heuristic optimization algorithms. The main inspiration of this algorithm came from multi-verse theory in physics. However, MVO like most optimization algorithms suffers from low convergence rate and entrapment in local optima. In this paper, a new chaotic multi-verse optimization algorithm (CMVO) is proposed to overcome these problems. The proposed CMVO is applied on 13 benchmark functions and 7 well-known design problems in the engineering and mechanical field; namely, three-bar trust, speed reduce design, pressure vessel problem, spring design, welded beam, rolling element-bearing and multiple disc clutch brake. In the current study, a modified feasible-based mechanism is employed to handle constraints. In this mechanism, four rules were used to handle the specific constraint problem through maintaining a balance between feasible and infeasible solutions. Moreover, 10 well-known chaotic maps are used to improve the performance of MVO. The experimental results showed that CMVO outperforms other meta-heuristic optimization algorithms on most of the optimization problems. Also, the results reveal that sine chaotic map is the most appropriate map to significantly boost MVO’s performance.     

Performance improvement of aircraft pitch angle control using a new reduced order fractionalized PID controller

In this paper, a new optimal reduced order fractionalized PID (ROFPID) controller based on the Harris Hawks Optimization Algorithm (HHOA) is proposed for aircraft pitch angle control. Statistical tests, analysis of the index of performance, and disturbance rejection, as well as transient and frequency responses, were all used to validate the effectiveness of the proposed approach. The performance of the proposed HHOA-ROFPID and HHOA-ROFPID controllers with Oustaloup and Matsuda approximations was then compared not only to the PID controller tuned by the original HHO algorithm but also to other controllers tuned by cutting-edge meta-heuristic algorithms such as the atom search optimization algorithm (ASOA), Salp Swarm Algorithm (SSA), sine-cosine algorithm (SCA), and Grey wolf optimization algorithm (GOA). Simulation results show that the proposed controller with the Matsuda approximation provides better and more robust performance compared to the proposed controller with the Oustaloup approximation and other existing controllers in terms of percentage overshoot, settling time, rise time, and disturbance rejection.     

Hybridization of Chaos and Flower Pollination Algorithm over K-Means for data clustering

Classical clustering algorithms like K-means often converge to local optima and have slow convergence rates for larger datasets. To overcome such situations in clustering, swarm based algorithms have been proposed. Swarm based approaches attempt to achieve the optimal solution for such problems in reasonable time. Many swarm based algorithms such as Flower Pollination Algorithm (FPA), Cuckoo Search Algorithm (CSA), Black Hole Algorithm (BHA), Bat Algorithm (BA) Particle Swarm Optimization (PSO), Firefly Algorithm (FFA), Artificial Bee Colony (ABC) etc have been successfully applied to many non-linear optimization problems. In this paper, an algorithm is proposed which hybridizes Chaos Optimization and Flower Pollination over K-means to improve the efficiency of minimizing the cluster integrity. The proposed algorithm referred as Chaotic FPA (CFPA) is compared with FPA, CSA, BHA, BA, FFA, and PSO over K-Means for data clustering problem. Experiments are conducted on sixteen benchmark datasets. Algorithms are compared on four different performance parameters — cluster integrity, execution time, number of iterations to converge (NIC) and stability. Results obtained are analyzed statistically using Non-parametric Friedman test. If Friedman test rejects the Null hypothesis then pair wise comparison is done using Nemenyi test. Experimental Result demonstrates the following: (a) CFPA and BHA have better performance on the basis of cluster integrity as compared to other algorithms; (b) Prove the superiority of CFPA and CSA over others on the basis of execution time; (c) CFPA and FPA converges earlier than other algorithms to evaluate optimal cluster integrity; (d) CFPA and BHA produce more stable results than other algorithms.     

考虑多种训练方式的自适应最有价值球员算法

最有价值球员算法（MVPA）是一种模拟体育比赛的新型智能优化算法,为解决其寻优精度低和收敛速度慢等问题，提出一种考虑多种训练方式的自适应最有价值球员算法（ACMTM-MVPA）。MVPA的初始化方式单一，随机性和盲目性强，降低了算法的收敛速度和寻求精度。为了增强初始化球员的水平，提高初始球队的整体实力，在MVPA的竞争阶段之前加入了训练阶段，并在训练阶段使用邻域搜索算法以及混沌序列和反向学习算法来训练和筛选球员；为了增强球员的自我探索能力以及向最佳球员学习的能力，使球员具有争夺最有价值球员奖杯的资格，在队伍竞争阶段加入了自适应的球员进化因子。对15个标准函数的测试结果表明，ACMTM-MVPA与MVPA、粒子群优化（PSO）算法和遗传算法（GA）相比，在寻优精度和收敛速度上更有优势。最后给出了ACMTM-MVPA在暴雨强度公式参数优化中的应用实例，结果显示，该算法法明显优于自适应光学优化算法、传统回归法与优选回归法。     

基于混沌搜索的思维进化算法

针对思维进化算法中的产生初始种群的盲目随机性和冗余性以及现有搜索方式易陷入局部最优的问题,将混沌优化和思维进化算法结合,提出了一种基于混沌搜索的思维进化算法（Chaos Mind Evaluation Algorithm,CMEA）。该算法在进化的不同阶段引入混沌优化操作,利用混沌的遍历性提高算法的收敛速度,克服了早熟现象,同时利用思维进化算法的记忆特性和当代最优解指导混沌搜索,提高算法的搜索能力。仿真结果表明,与标准思维进化相比,该算法优化能力强,能有效地避免局部收敛,具有更快的收敛速度。     

基于SSA算法的飞行动作规则自动提取

针对空战知识获取问题展开研究，提出了一条从海量飞行参数中获取知识的途径。构建空战专家系统知识库；对于飞行动作规则知识的提取，提出了一种基于樽海鞘群优化算法的飞行动作规则知识提取方法，为了使提取的规则知识简洁有效，对算法的评价函数进行了设计。通过对水平右转弯机动动作和斤斗动作进行规则提取仿真与分析，验证了该方法的有效性和可行性。     

基于随机鞭策机制的散漫度粒子群算法

针对标准粒子群算法全局搜索能力差、易陷入早熟等问题,提出了基于随机鞭策机制的散漫度粒子群算法。首先,给出了粒子散漫度概念,通过动态地对各个粒子的散漫程度进行评估,判断粒子状态,并通过随机鞭策机制处理散漫粒子,避免算法陷入局部最优;其次,对积极运动的粒子利用个体历史最优位置进行处理,加快算法收敛速度;对11个标准函数进行测试,并与标准粒子群算法和其他改进算法进行对比,实验结果表明,基于散漫度的快速收敛粒子群算法寻优精度更高,收敛速度更快。     

Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images

This paper proposes an improved variant of Darwinian Particle Swarm Optimization algorithm based on chaotic functions. Most of the evolutionary algorithms faces the problem of getting trapped in local optima in its search for global optimum solutions. This is highly influenced by the use of random sequences by different operators in these algorithms along their run. The proposed algorithm replaces random sequences by chaotic sequences mitigating the problem of premature convergence. Experiments were conducted to investigate the efficiency of 10 defined chaotic maps and the best one was chosen. Performance of the proposed Chaotic Darwinian Particle Swarm Optimization (CDPSO) algorithm is compared with chaotic variants of optimization algorithms like Cuckoo Search, Harmony Search, Differential Evolution and Particle Swarm Optimization exploiting the chosen optimal chaotic map. Various histogram thresholding measures like minimum cross entropy and Tsallis entropy were used as objective functions and implemented for satellite image segmentation scenario. The experimental results are validated qualitatively and quantitatively by evaluating the mean, standard deviation of the fitness values, PSNR, MSE, SSIM and the total time required for the execution of each optimization algorithm.     

带柯西变异的自学习改进烟花算法

针对烟花算法收敛速度慢和求解精度不高,论文提出了一种改进烟花算法--带柯西变异的自学习改进烟花算法.改进算法用全局搜索能力更强的柯西变异算子替代高斯变异算子,增大变异范围;用全局最优烟花个体和历史柯西火花的位置来构造新的爆炸半径使其不仅能够继承和学习历史信息,还能够自适应地调整步长;并使用可同时兼顾烟花质量与分布的"精英-随机"选择策略.使用了10个典型基准测试函数和10个0-1背包问题进行仿真实验,结果表明,与蝙蝠算法、粒子群算法、带高斯扰动的粒子群算法、烟花算法、增强烟花算法、自适应烟花算法相比.该算法在收敛速度、计算精度以及稳定性方面性能更优.     

改进的变尺度混沌粒子群算法及其应用

针对粒子群算法(PSO)存在局部最优及后期收敛速度慢等问题,提出一种改进的变尺度混沌粒子群算法(IMCPSO).该算法初期,在整个解空间对最优粒子进行变尺度混沌扰动,以防止陷入局部最优;算法后期,则以最优粒子为中心引入变尺度混沌扰动,以提高算法收敛速度.当算法一旦陷入局部最优时,采用混沌粒子替代部分种群粒子以增加粒子多样性,使算法尽快跳出局部最优.基于benchmark测试函数的仿真结果表明,所提算法与基本粒子群算法(SPSO)和变尺度混沌粒子群算法(MCPSO)相比,具有明显好的搜索精度和收敛速度.最后,将该算法应用于电路故障诊断实验中的支持向量机参数优化问题,实验结果说明了其应用价值.