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.     

An efficient algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA)

The league championship algorithm (LCA) is a new algorithm originally proposed for unconstrained optimization which tries to metaphorically model a League championship environment wherein artificial teams play in an artificial league for several weeks (iterations). Given the league schedule, a number of individuals, as sport teams, play in pairs and their game outcome is determined given known the playing strength (fitness value) along with the team formation (solution). Modelling an artificial match analysis, each team devises the required changes in its formation (a new solution) for the next week contest and the championship goes for a number of seasons. In this paper, we adapt LCA for constrained optimization. In particular: (1) a feasibility criterion to bias the search toward feasible regions is included besides the objective value criterion; (2) generation of multiple offspring is allowed to increase the probability of an individual to generate a better solution; (3) a diversity mechanism is adopted, which allows infeasible solutions with a promising objective value precede the feasible solutions. Performance of LCA is compared with comparator algorithms on benchmark problems where the experimental results indicate that LCA is a very competitive algorithm. Performance of LCA is also evaluated on well-studied mechanical design problems and results are compared with the results of 21 constrained optimization algorithms. Computational results signify that with a smaller number of evaluations, LCA ensures finding the true optimum of these problems. These results encourage that further developments and applications of LCA would be worth investigating in the future studies.     

A new adaptive mutative scale chaos optimization algorithm and its application

Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.     

An adaptive single-point algorithm for global numerical optimization

This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution.     

A new adaptive mutative scale chaos optimization algorithm and its application

Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin(2/x) with infinite collapses within the finite region[-1;1] to some representative iterative chaotic maps with finite collapses (e.g., Logistic map, Tent map, and Chebyshev map), a new adaptive mutative scale chaos optimization algorithm (AMSCOA) is proposed by using the chaos model x = in(2/x). In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken: 1) the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2) the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.     

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。     

增强型野马优化算法及其工程应用

针对野马优化算法易陷入局部最优、收敛速度慢等缺点,提出增强型野马优化算法。首先,在种群初始化阶段,采用Sinusoidal映射,增加种群的多样性;其次,在阶段更新过程中,设计出非线性收敛性更强的自适应权重,调节全局搜索和局部优化能力;然后,在更新领导者位置阶段加入扰动因子,平衡局部和全局探索能力;进一步,利用自适应t分布变异,对个体位置进行扰动,提高算法跳出局部最优的能力。通过在CEC2021测试竞赛进行测试优化比较,验证算法的有效性和稳健性,并利用Wilcoxon秩和检验和MAE排名,验证算法的有效性。最后将算法应用到工程难题问题中,验证了该算法在工程优化问题上的适用性与优越性。实验结果表明,与其他智能算法相比,增强型野马优化算法具有更强的寻优能力和更快的收敛速度。     

自适应变尺度混沌免疫优化算法及其应用

结合混沌优化算法与免疫算法的特点,提出了一种采用折叠次数无限的自映射X=sin(2/x)产生混沌变量的自适应变尺度混沌免疫优化算法.该算法通过自适应变尺度方法不断调整优化变量的搜索空间,同时采用最大循环次数作为控制指标,既保证了寻优的准确性,又保证了算法的快速性.应用该算法对3个测试函数进行优化计算得到了比较满意的结果.将此算法应用于移动Ad Hoc网络入侵检测时的仿真实验结果表明,自适应变尺度混沌免疫优化算法能有效地减少对训练样本的依赖,同时减少噪音数据对入侵检测系统性能的影响,适用于移动自组网络对于入侵检测系统高检测率、高抗噪能力和低计算延迟的要求.     

动态分级中心引力约束优化算法及工程应用

结合非固定多段罚函数处理约束条件,提出一种动态分级中心引力优化算法用于求解约束优化问题。该算法利用佳点集初始化个体以保证种群的多样性。在每次迭代过程中将种群分为两个子种群,分别用于全局搜索和局部搜索,根据搜索阶段动态调整子种群个体数目。对几个标准的测试问题和工程优化问题进行数值实验,结果表明该算法能处理不同的约束优化问题。     

一种改进的协同优化算法及其应用

针对协同优化方法收敛困难、优化效率低的问题,提出了一种改进的协同优化算法—ICO算法。通过引入自适应松弛因子将一致性等式约束转化为不等式约束,同时建立混合惩罚函数,将系统级约束优化问题转化为无约束优化问题,ICO算法较好地克服了传统协同优化算法难于收敛的缺点。标准算例实验结果表明,ICO算法能够有效提高优化的稳定性、可靠性和计算效率。优化结果显示了协同优化算法解决海洋供应船的设计优化问题的有效性,为解决更为复杂工程系统的设计优化问题奠定了基础。     

A human learning optimization algorithm and its application to multi-dimensional knapsack problems

Inspired by human learning mechanisms, a novel meta-heuristic algorithm named human learning optimization (HLO) is presented in this paper in which the individual learning operator, social learning operator, random exploration learning operator and re-learning operator are developed to generate new solutions and search for the optima by mimicking the human learning process. Then HLO is applied to solve the well-known 5.100 and 10.100 multi-dimensional knapsack problems from the OR-library and the performance of HLO is compared with that of other meta-heuristics collected from the recent literature. The experimental results show that the presented HLO achieves the best performance in comparison with other meta-heuristics, which demonstrates that HLO is a promising optimization tool.     

Ions motion algorithm for solving optimization problems

This paper proposes a novel optimization algorithm inspired by the ions motion in nature. In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization. The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity, fast convergence, and high local optima avoidance. The performance of this algorithm is benchmarked on 10 standard test functions and compared to four well-known algorithms in the literature. The results demonstrate that the proposed algorithm is able to show very competitive results and has merits in solving challenging optimization problems.     

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.     

正交文化算法及其在布局优化问题中的应用

布局优化问题是工程应用中普遍存在的一种组合优化问题,属于NP完备问题。针对布局优化问题,将差异演化算法和郭涛算法融入文化算法的框架,利用正交设计方法初始化种群,提出了一种正交文化算法。通过对一个带约束的和一个较大规模的不带约束的布局优化问题进行性能比较,验证了该算法的可行性和有效性。     

An enhanced artificial hummingbird algorithm and its application in truss topology engineering optimization

In view of the shortcomings such as slow search speed, low optimization precision and premature convergence of artificial hummingbird algorithm, an enhanced artificial hummingbird algorithm based on golden sine factor named DGSAHA is proposed. Firstly, chaos mapping is used to generate the initial candidate solution to increase the diversity of the population, which lays the foundation for the global search. Then, perturb the individuals by means of the differential variation between individuals on the group, thereby enhancing the diversity of the population, preserving the excellent individuals, eliminating the inferior individuals, and guiding the search process to approach the global optimal solution, avoiding the phenomenon of premature convergence. Finally, the golden sine factor were introduced in the guided foraging stage is conducive to the full exploration of the global optimal solution, reducing the search space for individuals to approach the optimal solution. And, it facilitates the balance between “exploration” and “exploitation” of algorithm. Thereby, the accuracy and speed of the DGSAHA can be improved to a certain extent. 25 classic functions, the CEC2014 and CEC2019 benchmark functions were tested, and several representative meta-heuristic algorithms and its improved algorithm are compared for evaluate the validity of DGSAHA. Meanwhile, the dimensional scalability of the variable-dimensional test function is discussed. The results of non-parametric statistical analysis and performance index show that DGSAHA in this paper has better comprehensive optimization performance, significantly improves the search speed and convergence precision, and has strong ability to get rid of the local optimal solution. Finally, the performance of DGSAHA and the practicability of truss structure are answered by three engineering examples of plane and space truss topology optimization problem. This optimization problem considers not only the static constraints such as stress, displacement and buckling, but also the dynamic constraints of frequency and motion stability. In order to avoid singularity and unnecessary analysis, the stiffness, mass and load matrices are reconstructed in finite element analysis. A lighter truss structure than the existing solution is obtained. The validity, extensibility and practicability of the algorithm are further illustrated.     

改进的粒子群算法在化工过程优化中的应用

在现有自适应粒子群优化算法的研究基础上本文引入1种反弹机制(Rebound Mechanism),提出了1种改进的粒子群算法——反弹自适应粒子群优化算法。RAPSO能在搜索过程中充分利用粒子的飞行速度和方向等信息(下文称为动量信息),维持粒子的多样性以提升算法的搜索性能。通过比较,本文提出的RAPSO在一定程度上改进了现有的自适应粒子群算法的优化性能。运用RAPSO对催化裂化装置进行优化试验,其结果表明无论在单变量优化还是在多变量优化中,该装置的转化率都得到了一定程度的提高。     

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.     

基于初始路径优化的蚁群算法及应用

为解决蚁群算法在初始阶段执行效率低、信息素随机分布、路径杂乱无章的缺点,提出将正交设计方法引入初始优化中.创建正交离散过程,形成正交优化的路径设置;优化初始化过程,形成初始解;以动态概率转移规则来构造路径;精练的选路策略等4项改进措施的初始路径优化模型.该模型提高了算法的执行效率,模拟算例成功应用于连续域问题的饲料配方设计方面,表明该算法有效且可行,开辟了一条饲料配方设计的新途径,同时对蚁群算法解决连续域问题提供可参考技的模型和求解方法.     

An algorithm for computationally expensive engineering optimization problems

Abstract

Modern engineering design often relies on computer simulations to evaluate candidate designs, a scenario which results in an optimization of a computationally expensive black-box function. In these settings, there will often exist candidate designs which cause the simulation to fail, and can therefore degrade the search effectiveness. To address this issue, this paper proposes a new metamodel-assisted computational intelligence optimization algorithm which incorporates classifiers into the optimization search. The classifiers predict which candidate designs are expected to cause the simulation to fail, and this prediction is used to bias the search towards designs predicted to be valid. To enhance the search effectiveness, the proposed algorithm uses an ensemble approach which concurrently employs several metamodels and classifiers. A rigorous performance analysis based on a set of simulation-driven design optimization problems shows the effectiveness of the proposed algorithm.     

一种改进的多目标粒子群优化算法及其应用

针对多目标粒子群优化算法在求解约束优化问题时存在难以兼顾收敛性能和求解质量这一问题,提出一种基于免疫网络的改进多目标粒子群优化算法.该算法通过免疫网络互通种群最优信息达到粒子群算法与人工免疫网络算法的协同搜索,同时给出了速度迁移策略、自适应方差变异策略和基于聚类的免疫网络策略.最后将所提出的方法应用于求解电弧炉供电优化模型,达到了减少电量消耗、缩短冶炼时间、延长炉衬使用寿命的目的,同时表明了该算法的有效性.