求解约束优化问题的改进果蝇优化算法及其工程应用

针对基本果蝇优化算法收敛速度慢、求解精度低、易于陷入局部极值以及算法候选解不能取负值等不足,提出一种用于解决约束优化问题的改进果蝇优化算法.该算法利用果蝇个体历史最佳记忆信息和种群全局历史最佳记忆信息构建多策略混合协同进化的搜索机制,以达到有效平衡算法的全局探索与局部开发的目的,同时也能够较好地避免算法的早熟收敛问题;...     

求解复杂约束优化问题的多策略混合麻雀搜索算法

针对麻雀搜索算法面对具有强约束、非凸性和不可微特征的复杂问题所存在的开发与探索能力不平衡、易陷入局部最优、过早收敛和种群多样性较低等不足,提出一种求解复杂约束优化问题的多策略混合麻雀搜索算法.首先,利用反向学习策略构建双向初始化机制,以达到获得分布更优的初始种群的目的;其次,设计一种基于交叉与变异算子的位置更新公式,扩大搜索范围,丰富搜索机制,以平衡算法探索和开发能力,同时提高算法的收敛精度和速度;最后,使用社区学习策略对种群进行精炼,强化开发能力与跳出局部极值的能力,并保持种群的多样性.分别在CEC2017的28个实数约束优化问题和1个工程优化问题上进行了性能评估,实验结果表明,所提出的算法对比其他优化算法具有寻优能力强、收敛精度高、收敛速度快等优势,可有效解决复杂约束优化问题.     

求解约束化工优化问题的混合布谷鸟搜索算法

针对布谷鸟搜索算法存在收敛速度慢和易陷入局部最优等缺陷,提出一种基于Rosenbrock搜索和柯西变异的混合布谷鸟搜索算法用于求解约束化工优化问题。该算法首先采用佳点集方法对鸟窝位置进行初始化,为全局搜索的多样性奠定基础;然后利用Rosenbrock搜索算法对当前最优位置进行局部搜索,以提高算法的收敛速度;最后对当前最优解进行柯西变异以避免算法陷入局部最优。两个约束化工优化问题的实验结果表明了该混合算法的有效性。     

A poly-hybrid PSO optimization method with intelligent parameter adjustment

This paper presents results on a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within the hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.     

Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems.

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations.     

融合改进天牛须搜索的教与学优化算法

针对教与学优化算法易早熟,解精度低,甚至收敛于局部最优的问题,提出一种新的融合改进天牛须搜索的教与学优化算法.该算法利用Tent映射反向学习策略初始化种群,提升初始解质量.在"教"阶段,对教师个体执行天牛须搜索算法,增强教师教学水平,提高最优解的精确性.在"学"阶段,对学生个体进行混合变异,从而跳出局部最优,平衡算法的...     

电力系统PMU最优配置问题的混合优化算法

针对电力系统潮流计算方程直接可解的PMU最优配置问题,提出了一种引入小生境技术的遗传禁忌搜索混合算法。混合优化算法以小生境遗传算法为主体,避免传统遗传算法“早熟”和解的多样性不足的问题;结合禁忌搜索思想,使用TSR算子进行交叉操作,解决传统遗传算法局部搜索能力较差和收敛速度有待提高的问题。用该算法与其他两种传统算法进行了对比验证,结果表明该混合算法不仅能寻得全局最优解,而且提供了解的多样性,提高了优化效率,具有广阔的应用前景。     

求解优化问题的混合PSO-Solver算法

融合了粒子群算法(PSO) 和Solver 加载宏,形成混合PSO-Solver算法进行优化问题的求解。PSO作为全局搜索算法首先给出问题的全局可行解,Solver则是基于梯度信息的局部搜索工具,对粒子群算法得出的解再进行改进,二者互相结合,既加快了全局搜索的速度,又有效地避免了陷入局部最优。算法用VBA语言进行编程,简单且易于实现。通过对无约束优化问题和约束优化问题的求解,以及和标准PSO、其他一些混合算法的比较表明,PSO-Solver算法能够有效地提高求解过程的收敛速度和解的精确性。     

生产调度优化问题文化进化算法研究

许多生产调度优化问题属于NP-hard问题,其求解通常采用智能启发式算法。基于文化算法及文化进化思想设计的文化进化算法,通过上层文化空间的经验知识指导下层个体进化搜索的方向及步长,通过模拟人类社会文化进化的机制实现文化空间的进化与更新,最后将算法应用到置换Flow shop问题的求解,用Matlab编程仿真测试,结果表明此算法解决生产调度优化问题是可行的,而且其全局搜索性能优于一种改进的GA算法。     

求解作业车间调度问题的混合帝国主义竞争算法

针对最小化最大完工时间的作业车间调度问题（JSP）,提出一种结合帝国主义竞争算法（ICA）和禁忌搜索（TS）算法的混合算法。混合算法以帝国主义竞争算法为基础,在同化操作中融入遗传算法中的杂交算子和变异算子,使算法全局搜索能力更强。为了克服帝国主义竞争算法局部搜索能力弱的缺点,引入禁忌搜索算法进一步优化同化操作后的后代。禁忌搜索算法采用混合邻域结构和新型选择策略,使得算法能够更有效地搜索邻域解。混合算法兼具全局搜索能力和局部搜索能力,通过对13个经典的Benchmark调度问题进行仿真测试,并与近年4种新型混合算法进行对比分析,实验结果表明了所提算法求解Job Shop调度问题的有效性和稳定性。     

Metaheuristic methods in hybrid flow shop scheduling problem

Memetic algorithms are hybrid evolutionary algorithms that combine global and local search by using an evolutionary algorithm to perform exploration while the local search method performs exploitation. This paper presents two hybrid heuristic algorithms that combine particle swarm optimization (PSO) with simulated annealing (SA) and tabu search (TS), respectively. The hybrid algorithms were applied on the hybrid flow shop scheduling problem. Experimental results reveal that these memetic techniques can effectively produce improved solutions over conventional methods with faster convergence.     

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.

     

基于禁忌搜索与遗传算法的案例检索技术

案例的检索和提取是案例推理系统的一个关键步骤,案例检索结果的优劣直接影响到案例重用、修改以及整个系统的性能。遗传算法是一种基于进化思想的全局优化方法,但是存在搜索速度慢以及早熟收敛等问题;禁忌搜索是一种局部优化技术,具有搜索速度快等优点。文中将禁忌算法和遗传算法结合在一起提出了一种新的聚类方法,并将该聚类方法引入大型案例推理系统的案例检索过程中。实验结果表明使用这种方法能够达到较理想的搜索效果。     

基于遗传和禁忌搜索算法求解车间调度优化问题

针对柔性生产环境下的车间调度问题,在考虑遗传算法早熟收敛问题和禁忌搜索法自适应优点的基础上,将遗传算法和禁忌搜索法结合起来,提出了基于遗传和禁忌搜索的混合动态优化调度算法,并用实例对该算法进行了仿真研究。结果表明,此算法有很好收敛精度,是可行的,并且能够在扰动发生后提供新的调度计划,与传统的调度算法相比较,体现了明显的优越性。     

基于混合策略改进的果蝇优化算法

针对基本果蝇优化算法收敛精度不高、容易陷入局部最优和收敛速度慢的问题,提出一种基于混合策略改进的果蝇优化算法(MSFOA)。受鲸鱼捕食猎物的启发,在对个体历史最优位置的更新中,采用新的组合搜索的方法,加快果蝇搜索迭代速度;在更新后的位置公式中引入自适应权重系数,提高算法的优化精度;当达到局部收敛状态时,结合多尺度高斯变异算子解决局部最优的限制。采用6个测试函数的仿真结果表明,MSFOA算法相比其它算法具有更快的收敛速度和较高的寻优精度。     

Improved accelerated PSO algorithm for mechanical engineering optimization problems

This paper introduces an improved accelerated particle swarm optimization algorithm (IAPSO) to solve constrained nonlinear optimization problems with various types of design variables. The main improvements of the original algorithm are the incorporation of the individual particles memories, in order to increase swarm diversity, and the introduction of two selected functions to control balance between exploration and exploitation, during search process. These modifications are used to update particles positions of the swarm. Performance of the proposed algorithm is illustrated through six benchmark mechanical engineering design optimization problems. Comparison of obtained computation results with those of several recent meta-heuristic algorithms shows the superiority of the IAPSO in terms of accuracy and convergence speed.     

一种快速收敛的混合遗传算法

利用遗传算法早熟的特点 ,构造出一种快速收敛的混合算法来求解优化问题 ,并分析了它的收敛性。它是使用遗传算法来生成搜索方向 ,从而保证了算法的收敛性。该算法利用遗传算法的全局搜索能力 ,并采用 Nelder- Mead单纯形法来加强算法的局部搜索能力 ,加快了算法的收敛速率。模拟实验表明 ,该方法具有高效性和鲁棒性     

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。     

求解带约束优化问题的混合式多策略萤火虫算法

目前多目标优化算法主要针对如何处理多个目标之间的冲突,对于如何处理约束考虑较少,鉴于此,提出一种求解带约束优化问题的混合式多策略萤火虫算法(HMSFA-PC).首先,提出一种改进的动态罚函数策略对约束优化问题进行预处理,将其转换为非约束优化问题;其次,对萤火虫算法本身进行改进,采用Lévy flights搜索机制有效地增大搜索范围;接着,引入随机扩张因子改进算法吸引模型,使种群突破束缚,有效避免早熟收敛,提出自适应维度重组机制,根据不同迭代时期选择差异性较大的个体进行信息交互、相互学习.为检验算法处理无约束优化问题的性能,将其在基准测试函数上与部分典型算法进行比较;为检验算法处理约束优化问题的性能,将其在实际约束测试问题中与一些顶尖约束求解算法进行比较.结果表明,HMSFA-PC在处理无约束优化问题时具有收敛速度快、收敛精度高等优势,并且在动态罚函数的协作下求解实际约束优化问题时仍具有良好的优化性能.     

QoS-driven multicast tree generation using Tabu search

Many multimedia communication applications require a source to transmit messages to multiple destinations subject to Quality-of-Service (QoS) delay constraint. The problem to be solved is to find a minimum cost multicast tree where each source to destination path is constrained by a delay bound. This problem has been proven to be NP-complete. In this paper, we present a Tabu Search (TS) algorithm to construct a minimum cost delay bounded multicast tree. The proposed algorithm is then compared with many existing multicast algorithms. Results show that on almost all test cases, TS algorithm exhibits more intelligent search of the solution subspace and is able to find better solutions than other reported multicast algorithms.