Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

This paper presents an evolutionary algorithm, called the multi-objective symbiotic evolutionary algorithm (MOSEA), to solve a multi-objective FMS process planning (MFPP) problem with various flexibilities. The MFPP problem simultaneously considers four types of flexibilities related to machine, tool, sequence, and process and takes into account three objectives: balancing the machine workload, minimizing part movements, and minimizing tool changes. The MOSEA is modeled as a two-leveled structure to find a set of well-distributed solutions close to the true Pareto optimal solutions. To promote the search capability of such solutions, two main processes imitating symbiotic evolution and endosymbiotic evolution are introduced, together with an elitist strategy and a fitness sharing scheme. Evolutionary components suitable for the MFPP problem are provided. With a variety of test-bed problems, the performance of the proposed MOSEA is compared with those of existing multi-objective evolutionary algorithms. The experimental results show that the MOSEA is promising in solution convergence and diversity.     

A self-adaptive differential evolution algorithm for continuous optimization problems

This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.     

A two-leveled symbiotic evolutionary algorithm for clustering problems

Because of its unsupervised nature, clustering is one of the most challenging problems, considered as a NP-hard grouping problem. Recently, several evolutionary algorithms (EAs) for clustering problems have been presented because of their efficiency for solving the NP-hard problems with high degree of complexity. Most previous EA-based algorithms, however, have dealt with the clustering problems given the number of clusters (K) in advance. Although some researchers have suggested the EA-based algorithms for unknown K clustering, they still have some drawbacks to search efficiently due to their huge search space. This paper proposes the two-leveled symbiotic evolutionary clustering algorithm (TSECA), which is a variant of coevolutionary algorithm for unknown K clustering problems. The clustering problems considered in this paper can be divided into two sub-problems: finding the number of clusters and grouping the data into these clusters. The two-leveled framework of TSECA and genetic elements suitable for each sub-problem are proposed. In addition, a neighborhood-based evolutionary strategy is employed to maintain the population diversity. The performance of the proposed algorithm is compared with some popular evolutionary algorithms using the real-life and simulated synthetic data sets. Experimental results show that TSECA produces more compact clusters as well as the accurate number of clusters.     

基于共生策略的多模式进化算法

借鉴生物在生态环境中的共生策略;提出了一种多模式共生进化算法,该算法主要体现了生物面对生态压力可采用不同策略协同的思想,反映了生物在生态环境中进化的多样性和多层次性,该文将该算法应用于系统跟踪问题,实验结果表明此算具有良好的性能。     

An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling

This paper considers the integrated FMS (flexible manufacturing system) scheduling problem (IFSP) consisting of loading, routing, and sequencing subproblems that are interrelated to each other. In scheduling FMS, the decisions for the subproblems should be appropriately made to improve resource utilization. It is also important to fully exploit the potential of the inherent flexibility of FMS. In this paper, a symbiotic evolutionary algorithm, named asymmetric multileveled symbiotic evolutionary algorithm (AMSEA), is proposed to solve the IFSP. AMSEA imitates the natural process of symbiotic evolution and endosymbiotic evolution. Genetic representations and operators suitable for the subproblems are proposed. A neighborhood-based coevolutionary strategy is employed to maintain the population diversity. AMSEA has the strength to simultaneously solve subproblems for loading, routing, and sequencing and to easily handle a variety of FMS flexibilities. The extensive experiments are carried out to verify the performance of AMSEA, and the results are reported.     

重启进化提高较大搜索规模时柔性作业解质量

种群多样性下降导致的早熟收敛限制了进化算法的求解质量与搜索效率。为应对收敛,提高较大搜索规模时的求解质量,引入随机算法中重启策略。种群收敛时,利用算法前期搜索结果（优势元素）和新产生的随机元素重新构造新种群继续进化。提高柔性作业车间调度问题解质量对实际工业生产有重要的现实意义。将重构思想应用于协同进化算法求解复杂柔性作业调度问题并跟踪种群进化状态。仿真实验结果表明,改进算法在进化过程中维持了较好的种群多样性,大幅提高了算法求解复杂柔性作业调度的搜索性能,并可以简单通过扩大搜索规模提高作业调度解质量。     

利用优势元素改进进化算法求解柔性作业调度

进化算法进化过程中种群多样性的降低导致的收敛极大限制了进化算法的求解质量与搜索效率,通过扩大搜索规模并不能有效提高算法求解质量。在共生进化算法求解柔性作业调度的基础上,研究进化算法在较大搜索规模下的种群状态进化过程,并在进化过程向种群内批量加入优势元素,调整种群模式构成。仿真实验表明：与传统进化算法相比,进化过程中加入优势元素能有效提高算法的求解质量与搜索效率,在较短的时间内能得到较好的解,并且在较大搜索规模时表现了更好的搜索性能。     

基于内部罚函数的进化算法求解约束优化问题

为解决现有约束处理方法可行解的适应度函数不包含约束条件的问题,提出了一种内部罚函数候选解筛选规则.该候选解筛选规则分别对可行解和不可行解采用内部罚函数和约束违反度进行筛选,从而达到平衡最小化目标函数和满足约束条件的目的.以进化策略算法为基础,给出了基于内部罚函数候选解筛选规则的进化算法的一个实现.进一步地,从理论和实验角度分别验证了内部罚函数候选解筛选规则的有效性:以(1+1)进化算法为例,从进化成功率方面验证了内部罚函数候选解筛选规则的理论有效性;通过13个测试问题的数值实验,从进化成功率、候选解后代是可行解的比例、进化步长和收敛速度方面验证了内部罚函数候选解筛选规则的实验有效性.     

一种自适应多策略行为粒子群优化算法

针对粒子群优化算法收敛速度慢、局部搜索能力差等缺点,提出一种自适应多策略行为粒子群优化算法.算法中每个粒子拥有4种行为进化策略,在迭代过程中通过计算每种进化策略的立即价值、未来价值和综合奖励来决定粒子的进化行为,并通过策略行为概率变异算法提升个体寻优速度或避免陷入局部最优解.在经典的基准测试函数上,对新算法与其他7个群智能进化算法的测试结果进行比较分析,结果表明所提出算法具有很好的求解精度和收敛速度,尤其适合应用于一些高维优化问题.     

Efficient Self-Evolving Evolutionary Learning for Neurofuzzy Inference Systems

This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA is that it is based on evolutionary algorithms that can determine the number of fuzzy rules and adjust the NFIS parameters. The SEELA consists of structure learning and parameter learning. The structure learning attempts to determine the number of fuzzy rules. A subgroup symbiotic evolution is adopted to yield several variable fuzzy systems, and an elite-based structure strategy is adopted to find a suitable number of fuzzy rules for solving a problem. The parameter learning is to adjust parameters of the NFIS. It is a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm, called cultural CPSO (CCPSO). The CCPSO, which uses cooperative behavior among multiple swarms, can increase the global search capacity using the belief space. Experimental results demonstrate that the proposed method performs well in predicting time series and solving nonlinear control problems.      

独立自适应调整参数的粒子群优化算法

针对传统粒子群优化算法在求解复杂优化问题时易陷入局部最优和依赖参数的取值等问题,提出了一种独立自适应参数调整的粒子群优化算法。算法重新定义了粒子进化能力、种群进化能力以及进化率,在此基础上给出了粒子群惯性权重及学习因子的独立调整策略,更好地平衡了算法局部搜索与全局搜索的能力。为保持种群多样性,提高粒子向全局最优位置的收敛速度,在算法迭代过程中,采用粒子重构策略使种群中进化能力较弱的粒子向进化能力较强的粒子进行学习,重新构造生成新粒子。最后通过CEC2013中的10个基准测试函数与4种改进粒子群算法在不同维度下进行测试对比,实验结果验证了该算法在求解复杂函数时具有高效性,通过收敛性分析说明了算法的有效性。     

正交设计的E占优策略求解高维多目标优化问题研究

在实际应用中,传统多目标演化算法面临着高维多目标优化问题。针对这一缺陷,提出正交E占优(Orthogo-nality E-dominant,OE)策略。在OE策略的理论优越性设计的基础上,改进了当前5种具有代表性的演化多目标优化算法。改进前后的算法求解DTLZ1-6(20)测试问题的数值对比试验显示,OE策略改进后的算法在不同程度上提高了算法求解高维多目标优化问题的效果,从而证实了OE策略对演化多目标优化算法改进的有效性。     

Coevolutionary augmented Lagrangian methods for constrainedoptimization

This paper introduces a coevolutionary method developed for solving constrained optimization problems. This algorithm is based on the evolution of two populations with opposite objectives to solve saddle-point problems. The augmented Lagrangian approach is taken to transform a constrained optimization problem to a zero-sum game with the saddle point solution. The populations of the parameter vector and the multiplier vector approximate the zero-sum game by a static matrix game, in which the fitness of individuals is determined according to the security strategy of each population group. Selection, recombination, and mutation are done by using the evolutionary mechanism of conventional evolutionary algorithms such as evolution strategies, evolutionary programming, and genetic algorithms. Four benchmark problems are solved to demonstrate that the proposed coevolutionary method provides consistent solutions with better numerical accuracy than other evolutionary methods     

Human-competitive lens system design with evolution strategies

Lens system design provides ideal problems for evolutionary algorithms: a complex non-linear optimization task, often with intricate physical constraints, for which there is no analytical solutions. This paper demonstrates, through the use of two evolution strategies, namely non-isotropic Self-Adaptive evolution strategy (SA-ES) and Covariance Matrix Adaptation evolution strategy (CMA-ES), as well as multiobjective Non-Dominated Sort Genetic Algorithm 2 (NSGA-II) optimization, the human competitiveness of an approach where an evolutionary algorithm is hybridized with a local search algorithm to solve both a classic benchmark problem, and a real-world problem.     

A many-objective evolutionary algorithm based on a projection-assisted intra-family election

In recent years, many researchers have put emphasis on the study of how to keep a good balance between convergence and diversity in many-objective optimization. This paper proposes a new many-objective evolutionary algorithm based on a projection-assisted intra-family election. In the proposed algorithm, basic evolution directions are adaptively generated according to the current population and potential evolution directions are excavated in each individual's family. Based on these evolution directions, a strategy of intra-family election is performed in every family and elite individuals are elected as representatives of the specific family to join the next stage, which can enhance the convergence of the algorithm. Moreover, a selection procedure based on angles is used to maintain the diversity. The performance of the proposed algorithm is verified and compared with several state-of-the-art many-objective evolutionary algorithms on a variety of well-known benchmark problems ranging from 5 to 20 objectives. Empirical results demonstrate that the proposed algorithm outperforms other peer algorithms in terms of both the diversity and the convergence of the final solutions set on most of the test instances. In particular, our proposed algorithm shows obvious superiority when handling the problems with larger number of objectives.     

面向进制转换和克隆进化的帝国竞争改进算法

帝国竞争算法(imperialist competitive algorithm,ICA)是一种被广泛应用于求解各类理论与实践问题的随机搜索智能优化算法,但它收敛过快的特性令其容易在求解复杂问题时陷入局部最优,故对ICA进行有针对性的改进十分必要.引入二进制转换和克隆进化机制,为算法的进化种群提供新的上升通道和进化模式...     

Global replacement-based differential evolution with neighbor-based memory for dynamic optimization

Dynamic optimization problems challenge the evolutionary algorithms, owing to the diversity loss or the low search efficiency of the algorithms, especially when the problems change frequently. This paper presents a novel differential evolution algorithm to address the dynamic optimization problems. Unlike the most used “DE/rand/1” mutation operator, in this paper, the “DE/best/1” mutation is employed to generate a mutant individual. In order to enhance the search efficiency of differential evolution, the classical differential evolution algorithm is modified by a novel replacement operator, in which the worst individual in the whole population is replaced by the newly generated trial vector as a “steady-state” manner. During optimizing, some newly generated solutions are stored into a memory set, in which these stored solutions are located around the current best solution. When the environmental change is detected, the stored solutions are expected to guide the reinitialized solutions to track the new location of global optimum as soon as possible. The performance of the proposed algorithm is compared with six state-of-the-art dynamic evolutionary algorithms over some benchmark problems. The experimental results show that the proposed algorithm clearly outperforms the competitors.     

Self-Adaptive Multimethod Search for Global Optimization in Real-Parameter Spaces

Many different algorithms have been developed in the last few decades for solving complex real-world search and optimization problems. The main focus in this research has been on the development of a single universal genetic operator for population evolution that is always efficient for a diverse set of optimization problems. In this paper, we argue that significant advances to the field of evolutionary computation can be made if we embrace a concept of self-adaptive multimethod optimization in which multiple different search algorithms are run concurrently, and learn from each other through information exchange using a common population of points. We present an evolutionary algorithm, entitled A Multialgorithm Genetically Adaptive Method for Single Objective Optimization (AMALGAM-SO), that implements this concept of self adaptive multimethod search. This method simultaneously merges the strengths of the covariance matrix adaptation (CMA) evolution strategy, genetic algorithm (GA), and particle swarm optimizer (PSO) for population evolution and implements a self-adaptive learning strategy to automatically tune the number of offspring these three individual algorithms are allowed to contribute during each generation. Benchmark results in 10, 30, and 50 dimensions using synthetic functions from the special session on real-parameter optimization of CEC 2005 show that AMALGAM-SO obtains similar efficiencies as existing algorithms on relatively simple unimodal problems, but is superior for more complex higher dimensional multimodal optimization problems. The new search method scales well with increasing number of dimensions, converges in the close proximity of the global minimum for functions with noise induced multimodality, and is designed to take full advantage of the power of distributed computer networks.     

基于环境Pareto 支配选择策略的有约束多目标差分进化算法

在处理有约束多目标问题的进化算法中, 目前普遍采用Deb 教授提出的约束占优的直接支配选择策略. 在约束处理中, 优秀不可行解与优秀可行解同样重要, 但在直接支配选择策略中, 不可行解被选择的几率很小. 针对此问题, 设计一种环境Pareto 支配的选择策略, 并基于此提出用于解决有约束多目标问题的差分进化算法. 对经典测试函数进行仿真计算, 结果表明, 与其他算法相比, 所提出的算法具有更高的收敛性和稳定性.

     

Large scale evolutionary optimization using cooperative coevolution

Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectiveness and advantages when applied to large and complex problems, e.g., those with high dimensions. Although cooperative coevolution has been proposed as a promising framework for tackling high-dimensional optimization problems, only limited studies were reported by decomposing a high-dimensional problem into single variables (dimensions). Such methods of decomposition often failed to solve nonseparable problems, for which tight interactions exist among different decision variables. In this paper, we propose a new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems. A random grouping scheme and adaptive weighting are introduced in problem decomposition and coevolution. Instead of conventional evolutionary algorithms, a novel differential evolution algorithm is adopted. Theoretical analysis is presented in this paper to show why and how the new framework can be effective for optimizing large nonseparable problems. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 1000 dimensions. The results show clearly that our framework and algorithm are effective as well as efficient for large scale evolutionary optimisation problems. We are unaware of any other evolutionary algorithms that can optimize 1000-dimension nonseparable problems as effectively and efficiently as we have done.