Differential Evolution Algorithm Based on Ensemble of Constraint Handling Techniques and Multi-Population Framework

Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.

Composition of Web Services of Multi-Population Adaptive Genetic Algorithm Based on Cosine Improvement

Web quality of service (QoS) awareness requires not only the selection of specific services to complete specific tasks, but also the comprehensive quality of service of the whole web service composition. How to select the web service composition with the highest comprehensive QoS is a NP hard problem. In this paper, an improved multi population genetic algorithm is proposed. Cosine adaptive operator is added to the algorithm to avoid premature algorithm caused by improper genetic operator and the disadvantage of destroying excellent individuals in later period. Experimental results show that compared with the common genetic algorithm and multi population genetic algorithm, this algorithm has the advantages of shorter time consumption and higher accuracy, and effectively avoids the loss of effective genes in the population.

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

Multi-population particle swarm optimization algorithm for automatic design of steel frames

Steel structures are widely used;however,their traditional design method is a trial-and-error procedure which is neither efficient nor cost effective.Therefore,a multi-population particle swarm optimization(MPPSO)algorithm is developed to optimize the weight of steel frames according to standard design codes.Modifications are made to improve the algorithm performances including the constraint-based strategy,piecewise mean learning strategy and multi-population cooperative strategy.The proposed method is tested against the representative frame taken from American standards and against other steel frames matching Chinese design codes.The related parameter influences on optimization results are discussed.For the representative frame,MPPSO can achieve greater efficiency through reduction of the number of analyses by more than 65% and can obtain frame with the weight for at least 2.4%lighter.A similar trend can also be observed in cases subjected to Chinese design codes.In addition,a migration interval of 1 and the number of populations as 5 are recommended to obtain better MPPSO results.The purpose of the study is to propose a method with high efficiency and robustness that is not confined to structural scales and design codes.It aims to provide a reference for automatic structural optimization design problems even with dimensional complexity.The proposed method can be easily generalized to the optimization problem of other structural systems.

求解动态优化问题的多种群热力学遗传算法

多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法(multi-population based thermodynamic genetic algorithm,MPTDGA)。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞争学习,环境变化时分化成三个概率向量,并分别抽样产生原对偶和随机迁入三个子种群,依据这三个种群和记忆种群最好解的情况,选择新的工作概率向量进入新环境进行学习。在动态背包问题上的实验结果表明,MPTDGA比原对偶遗传算法跟踪最优解的能力更强,有很好的多样性,非常适合求解0-1动态优化问题。

Research on Financial Distress Prediction with Adaptive Genetic Fuzzy Neural Networks on Listed Corporations of China

To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.

Multi-objective test case prioritization based on multi-population cooperative particle swarm optimization

Test case prioritization(TCP) technique is an efficient approach to improve regression testing activities. With the continuous improvement of industrial testing requirements, traditional single-objective TCP is limited greatly, and multi-objective test case prioritization(MOTCP) technique becomes one of the hot topics in the field of software testing in recent years. Considering the problems of traditional genetic algorithm(GA) and swarm intelligence algorithm in solving MOTCP problems, such as falling into local optimum quickly and weak stability of the algorithm, a MOTCP algorithm based on multi-population cooperative particle swarm optimization(MPPSO) was proposed in this paper. Empirical studies were conducted to study the influence of iteration times on the proposed MOTCP algorithm, and compare the performances of MOTCP based on single-population particle swarm optimization(PSO) and MOTCP based on non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) with the MOTCP algorithm proposed in this paper. The results of experiments show that the TCP algorithm based on MPPSO has stronger global optimization ability, is not easy to fall into local optimum, and can solve the MOTCP problem better than TCP algorithm based on the single-population PSO and NSGA-Ⅱ.

An adaptive multi-population genetic algorithm for job-shop scheduling problem

Job-shop scheduling problem （JSP） is a typical NP-hard combinatorial optimization problem and has a broad background for engineering application. Nowadays, the effective approach for JSP is a hot topic in related research area of manufacturing system. However, some JSPs, even for moderate size instances, are very difficult to find an optimal solution within a reasonable time because of the process constraints and the complex large solution space. In this paper, an adaptive multi-population genetic algorithm （AMGA） has been proposed to solve this prob- lem. Firstly, using multi-populations and adaptive cross- over probability can enlarge search scope and improve search performance. Secondly, using adaptive mutation probability and elite replacing mechanism can accelerate convergence speed. The approach is tested for some clas- sical benchmark JSPs taken from the literature and com- pared with some other approaches. The computational results show that the proposed AMGA can produce optimal or near-optimal values on almost all tested benchmark instances. Therefore, we can believe that AMGA can be considered as an effective method for solving JSP.

Swarm Intelligence Research:From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence.Considering the importance of swarm intelli-gence for the future development of artificial intelligence,we discuss and analyze swarm intelligence from a broader and deeper perspect-ive.In a broader sense,we are talking about not only bio-inspired swarm intelligence,but also human-machine hybrid swarm intelli-gence.In a deeper sense,we discuss the research using a three-layer hierarchy:in the first layer,we divide the research of swarm intelli-gence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence;in the second layer,the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence;and in the third layer,we re-view single-population,multi-population and human-machine hybrid models from different perspectives.Single-population swarm intel-ligence is inspired by biological intelligence.To further solve complex optimization problems,researchers have made preliminary explor-ations in multi-population swarm intelligence.However,it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive in-telligent behavior that meets the needs of human cognition.Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence.In addition to single-population swarm intelligence,we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper.We also discuss the applications of swarm intelligence in optimization,big data analysis,unmanned systems and other fields.Finally,we discuss future research directions and key issues to be studied in swarm intelligence.