求解约束优化问题的改进型免疫算法

为了提高免疫算法求解约束优化问题的性能,给出了一种融合乘子法的免疫算法。设计了乘子法对约束条件的转换过程,给出了基于实数编码的克隆变异算子、浓度抑制算子和免疫算法框架,并对标准测试函数进行了实验验证。实验结果表明,该算法优于文献算法,具有较好的应用价值。     

一种新的求解动态连续优化的分层粒子群算法

为了高效求解动态连续优化问题,提出一种分层粒子群优化算法。该算法将动态函数定义域分成Q个子空间,每个空间用一个粒子群作为第一层进行独立搜索,Q个子空间的最优粒子再组成一个全局粒子群进行全局搜索,以达到全局牵引的作用,同时提出探测环境和响应环境的策略。利用经典的动态函数对算法进行测试,结果表明所提出算法能够迅速适应环境变化和跟踪最优解的变化,效果令人满意。     

一种求解动态优化问题的免疫文化基因算法

针对传统免疫网络动态优化算法局部寻优能力弱、寻优精度低及易早熟收敛的缺点,提出一种求解动态优化问题的免疫文化基因算法。基于文化基因算法基本框架,将人工免疫网络算法作为全局搜索算法,采用禁忌搜索算法作为局部搜索算子;同时引入柯西变异加强算法的全局搜索能力,并有效防止早熟收敛。通过对经典动态优化函数测试集在相同条件下的实验表明,该免疫文化基因算法相较于其他同类算法具有较好的搜索精度和收敛速度。     

A permutation-based dual genetic algorithm for dynamic optimization problems

Adaptation to dynamic optimization problems is currently receiving growing interest as one of the most important applications of genetic algorithms. Inspired by dualism and dominance in nature, genetic algorithms with the dualism mechanism have been applied for several dynamic problems with binary encoding. This paper investigates the idea of dualism for combinatorial optimization problems in dynamic environments, which are also extensively implemented in the real-world. A new variation of the GA, called the permutation-based dual genetic algorithm (PBDGA), is presented. Within this GA, two schemes based on the characters of the permutation in group theory are introduced: a partial-dualism scheme motivated by a new multi-attribute dualism mechanism and a learning scheme. Based on the dynamic test environments constructed by stationary benchmark problems, experiments are carried out to validate the proposed PBDGA. The experimental results show the efficiency of PBDGA in dynamic environments.     

A self-organizing random immigrants genetic algorithm for dynamic optimization problems

In this paper a genetic algorithm is proposed where the worst individual and individuals with indices close to its index are replaced in every generation by randomly generated individuals for dynamic optimization problems. In the proposed genetic algorithm, the replacement of an individual can affect other individuals in a chain reaction. The new individuals are preserved in a subpopulation which is defined by the number of individuals created in the current chain reaction. If the values of fitness are similar, as is the case with small diversity, one single replacement can affect a large number of individuals in the population. This simple approach can take the system to a self-organizing behavior, which can be useful to control the diversity level of the population and hence allows the genetic algorithm to escape from local optima once the problem changes due to the dynamics.     

Water cycle algorithm with evaporation rate for solving constrained and unconstrained optimization problems

This paper presents a modified version of the water cycle algorithm (WCA). The fundamental concepts and ideas which underlie the WCA are inspired based on the observation of water cycle process and how rivers and streams flow to the sea. New concept of evaporation rate for different rivers and streams is defined so called evaporation rate based WCA (ER-WCA), which offers improvement in search. Furthermore, the evaporation condition is also applied for streams that directly flow to sea based on the new approach. The ER-WCA shows a better balance between exploration and exploitation phases compared to the standard WCA. It is shown that the ER-WCA offers high potential in finding all global optima of multimodal and benchmark functions. The WCA and ER-WCA are tested using several multimodal benchmark functions and the obtained optimization results show that in most cases the ER-WCA converges to the global solution faster and offers more accurate results than the WCA and other considered optimizers. Based on the performance of ER-WCA on a number of well-known benchmark functions, the efficiency of the proposed method with respect to the number of function evaluations (computational effort) and accuracy of function value are represented.     

基于杂合机制的免疫遗传算法在动态问题中的应用

针对遗传算法在求解动态问题时存在多样性缺失,无法快速响应环境变化的问题,提出一种基于杂合子机制的免疫遗传算法．该算法借鉴免疫系统中多样性与记忆机理,从保持等位基因多样性出发,在免疫变异中引入杂合映射机制,使种群能够探索更大的解空间．同时,通过引入记忆策略,使算法迅速跟踪最优解变化轨迹．该方法在动态０-１优化问题的求解中取得了较好的效果．     

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.     

An improved firefly algorithm for solving dynamic multidimensional knapsack problems

There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively new and hot research area and has attracted notable attention of the researchers in the past decade. Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to evaluate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.     

一种求解函数优化问题的改进鲸鱼优化算法

为提高鲸鱼优化算法求解复杂函数优化问题的性能,提出一种基于自适应参数及小生境技术的改进鲸鱼优化算法。首先,引入自适应概率阈值协调算法的全局探索及局部开发能力;其次,利用自适应位置权重对鲸鱼位置更新公式进行调整,提高算法的收敛速度及寻优精度;最后,采用预选择小生境技术,避免算法出现早熟收敛的现象。通过对12个典型基准测试函数的仿真表明,改进算法的寻优精度和收敛速度较对比算法均有明显提升,证明了提出的改进策略能有效提高鲸鱼优化算法求解复杂函数优化问题的性能。     

An algorithm comparison for dynamic optimization problems

This work presents a study on the performance of several algorithms on different continuous dynamic optimization problems. Eight algorithms have been used: SORIGA (an Evolutionary Algorithm), an agents-based algorithm, the mQSO (a widely used multi-population PSO) as well as three heuristic-rule-based variations of it, and two trajectory-based cooperative strategies. The algorithms have been tested on the Moving Peaks Benchmark and the dynamic version of the Ackley, Griewank and Rastrigin functions. For each problem, a wide variety of configuration variations have been used, emphasizing the influence of dynamism, and using a full-factorial experimental design. The results give an interesting overview of the properties of the algorithms and their applicability, and provide useful hints to face new problems of this type with the best algorithmic approach. Additionally, a recently introduced methodology for comparing a high number of experimental results in a graphical way is used.     

求解动态优化问题的多群体UMDA

提出一种求解动态优化问题的多群体单变量边缘分布算法(MUMDA).首先,利用多个概率模型(对应多个群体)将搜索空间分成几个部分,通过对不同区域的搜索或探索将好解进行迁移,扩大搜索空间,增加种群多样性,跟踪最优解的变化;然后,利用对UMDA收敛性的证明分析了所提出算法的有效性;最后,对两个动态优化问题进行仿真计算,并与传统UMDA和基于随机迁移的UMDA(iUMDA)进行了比较.结果表明,MUMDA能快速适应环境的变化,跟踪最优解.     

Cognitive behavior optimization algorithm for solving optimization problems

Nature-based algorithms have become popular in recent fifteen years and have been widely applied in various fields of science and engineering, such as robot control, cluster analysis, controller design, dynamic optimization and image processing. In this paper, a new swarm intelligence algorithm named cognitive behavior optimization algorithm (COA) is introduced, which is used to solve the real-valued numerical optimization problems. COA has a detailed cognitive behavior model. In the model of COA, the common phenomenon of foraging food source for population is summarized as the process of exploration–communication–adjustment. Matching with the process, three main behaviors and two groups in COA are introduced. Firstly, cognitive population uses Gaussian and Levy flight random walk methods to explore the search space in the rough search behavior. Secondly, the improved crossover and mutation operator are used in the information exchange and share behavior between the two groups: cognitive population and memory population. Finally, the intelligent adjustment behavior is used to enhance the exploitation of the population for cognitive population. To verify the performance of our approach, both the classic and modern complex benchmark functions considered as the unconstrained functions are employed. Meanwhile, some well-known engineering design optimization problems are used as the constrained functions in the literature. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of COA for global numerical and engineering optimization problems.     

A new hybrid approach for dynamic continuous optimization problems

A new hybrid approach for dynamic optimization problems with continuous search spaces is presented. The proposed approach hybridizes efficient features of the particle swarm optimization in tracking dynamic changes with a new evolutionary procedure. In the proposed dynamic hybrid PSO (DHPSO) algorithm, the swarm size is varied in a self-regulatory manner. Inspired from the microbial life, the particles can reproduce infants and the old ones die. The infants are especially reproduced by high potential particles and located near the local optimum points, using the quadratic interpolation method. The algorithm is adapted to perform in continuous search spaces, utilizing continuous movement of the particles and using Euclidian norm to define the neighborhood in the reproduction procedure. The performance of the new proposed approach is tested against various benchmark problems and compared with those of some other heuristic optimization algorithms. In this regard, different types of dynamic environments including periodic, linear and random changes are taken with different performance metrics such as real-time error, offline performance and offline error. The results indicate a desirable better efficiency of the new algorithm over the existing ones.     

一种求解约束优化问题的混合算法

提出一种基于修改增广Lagrange函数和PSO的混合算法用于求解约束优化问题。将约束优化问题转化为界约束优化问题,混合算法由两层迭代结构组成,在内层迭代中,利用改进PSO算法求解界约束优化问题得到下一个迭代点。外层迭代主要修正Lagrange乘子和罚参数,检查收敛准则是否满足,重构下次迭代的界约束优化子问题,检查收敛准则是否满足。数值实验结果表明该混合算法的有效性。     

多目标优化问题的蚁群算法研究

将离散空间问题求解的蚁群算法引入连续空间,针对多目标优化问题的特点,提出一种用于求解带有约束条件的多目标函数优化问题的蚁群算法.该方法定义了连续空间中信息量的留存方式和蚂蚁的行走策略,并将信息素交流和基于全局最优经验指导两种寻优方式相结合,用以加速算法收敛和维持群体的多样性.通过3组基准函数来测试算法性能,并与NSGAII算法进行了仿真比较.实验表明该方法搜索效率高,向真实Pareto前沿逼近的效果好,获得的解的散布范围广,是一种求解多目标优化问题的有效方法.     

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。     

应用于高维优化问题的免疫进化算法

针对免疫算法在伞局优化过程中多样性不足的问题,提出一种新型的免疫进化算法.随机克隆扩张和多受体随机编辑算了足该算法的主要特色,同时引入改进的超变异算了加强个体的学习能力;提出一种新的算法件能评价准则,以比较不同算法在全局优化中的表现.实验环节中,首先确定了克降扩张比:然后将免疫进化算法与快速克降算法和Opt-IMMAL...     

A self-adaptive linear evolutionary algorithm for solving constrained optimization problems

In many real-world applications of evolutionary algorithms, the fitness of an individual requires a quantitative measure. This paper proposes a self-adaptive linear evolutionary algorithm (ALEA) in which we introduce a novel strategy for evaluating individual’s relative strengths and weaknesses. Based on this strategy, searching space of constrained optimization problems with high dimensions for design variables is compressed into two-dimensional performance space in which it is possible to quickly identify ‘good’ individuals of the performance for a multiobjective optimization application, regardless of original space complexity. This is considered as our main contribution. In addition, the proposed new evolutionary algorithm combines two basic operators with modification in reproduction phase, namely, crossover and mutation. Simulation results over a comprehensive set of benchmark functions show that the proposed strategy is feasible and effective, and provides good performance in terms of uniformity and diversity of solutions.