A new improved Alopex-based evolutionary algorithm and its application to parameter estimation

In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.

Finding Global Minima with a New Dynamical Evolutionary Algorithm

A new dynamical evolutionary algorithm (DEA) based on the theory of statistical mechanics is presented. This algorithm is very different from the traditional evolutionary algorithm and the two novel features are the unique of selecting strategy and the determination of individuals that are selected to crossover and mutate. We use DEA to solve a lot of global optimization problems that are nonlinear, multimodal and multidimensional and obtain satisfactory results.

Reconstructing the Nonlinear Dynamical Systems by Evolutionary Computation Techniques

We introduce a new dynamical evolutionary algorithm（DEA） based on the theory of statistical mechanics and investigate the reconstruction problem for the nonlinear dynamical systems using observation data. The convergence of the algorithm is discussed. We make the numerical experiments and test our model using the two famous chaotic systems （mainly the Lorenz and Chen systems）. The results show the relatively accurate reconstruction of these chaotic systems based on observational data can be obtained. Therefore we may conclude that there are broad prospects using our method to model the nonlinear dynamical systems.

Parameter optimization of electric bus transmission system based on dynamical evolutionary algorithm

The transmission ratio is the key parameters influence power performance and economic performance of electric vehicle （EV）. As a class of heuristic algorithms, Dynamical Evolutionary Algorithm （DEA） is suitable to solve multi-objective optimization problems. This paper presents a new method to optimize the transmission ratio using DEA. The fuzzy constraints and objective function of transmission ratio are established for parameter optimization problem of electric bus transmission. DEA is used to solve the optimiza- tion problem. The transmission system is also designed based on the optimization result. Optimization and test results show that the dynamical evolutionary algorithm is an effective method to solve transmission parameter optimization problems.

Evolutionary computation in China： A literature survey

Evolutionary computation （EC） has received significant attention in China during the last two decades. In this paper, we present an overview of the current state of this rapidly growing field in China. Chinese research in theoretical foundations of EC, EC-based optimization, EC-based data mining, and EC-based real-world applications are summarized.

Orbit Design for Responsive Space Using Multiple-objective Evolutionary Computation

Responsive orbits have exhibited advantages in emergencies for their excellent responsiveness and coverage to targets.Generally,there are several conflicting metrics to trade in the orbit design for responsive space.A special multiple-objective genetic algorithm,namely the Nondominated Sorting Genetic AlgorithmⅡ(NSGAⅡ),is used to design responsive orbits.This algorithm has considered the conflicting metrics of orbits to achieve the optimal solution,including the orbital elements and launch programs of responsive vehicles.Low-Earth fast access orbits and low-Earth repeat coverage orbits,two subtypes of responsive orbits,can be designed using NSGAI under given metric tradeoffs,number of vehicles,and launch mode.By selecting the optimal solution from the obtained Pareto fronts,a designer can process the metric tradeoffs conveniently in orbit design.Recurring to the flexibility of the algorithm,the NSGAI promotes the responsive orbit design further.

Chemical process dynamic optimization based on hybrid differential evolution algorithm integrated with Alopex

To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.

基于Alopex的进化优化算法

提出了一种基于Alopex的进化优化算法。该算法在进化过程中从种群中随机选择2个个体,通过计算2个个体和目标函数值的变化情况,确定算法进一步搜索方向的概率,逐步迭代最终收敛到全局最优。该算法具备基本进化算法的特点,同时具备Alopex算法的优点,即在一定程度上具有梯度下降法和模拟退火算法的优点。对典型函数的测试表明:新算法的全局搜索能力有了显著提高,特别是对多峰函数能够有效地避免早熟收敛问题。

基于Alopex的进化算法及其在乙炔加氢反应器建模中的应用

分析进化优化算法的共性,参考Alopex优化思想提出一种新的进化优化算法。给出该算法的具体步骤,将该算法与标准粒子群算法、遗传算法和差分算法在性能上做了仿真对比,并将其应用于乙炔加氢反应器出口乙炔浓度软测量的建模中。这种新的优化算法具有全局收敛能力,并具有较快的收敛速度。对典型函数的测试和基于神经网络的软测量建模表明:新算法具有较强的全局搜索能力,特别是对易于陷入局部最优的多峰函数能够有效地避免早熟收敛问题。

利用Alopex改进的动力学演化算法

通过将动力学演化算法(Dynamical Evolutionary Algorithm,DEA)与一种随机优化方法——Alopex算法相结合,提出一种改进的动力学演化算法。改进的算法改善了动力学演化算法摆脱局部极小点的能力,对典型函数的测试表明:改进算法的全局搜索能力有了显著提高,特别是对多峰函数能够有效地避免早熟收敛问题。

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

基于Alopex的野草算法

针对野草算法存在求解精度不高、收敛速度慢的问题,提出一种基于Alopex的野草算法。在原有野草算法框架的空间扩散阶段引入Alopex算法,通过从父代和子代个体自变量和目标函数值的变化情况获得启发信息,指导种群向最优方向进化。结合后的算法能够充分发挥两者的优点,改善野草算法收敛速度以及寻优精度。对典型基准函数的测试结果表明,该算法要优于基本野草算法,表现更为稳定,体现出较好的全局搜索能力,具有更快的收敛速度和更高的寻优精度,更适合于解决其它算法难以解决的高维多峰值函数的优化问题;通过与其它相关智能算法的比较,进一步验证了该算法的有效性。

A survey on algorithm adaptation in evolutionary computation

Evolutionary computation （EC） is one of the fastest growing areas in computer science that solves intractable optimization problems by emulating biologic evolution and organizational behaviors in nature. To de- sign an EC algorithm, one needs to determine a set of algorithmic configurations like operator selections and parameter settings. How to design an effective and ef- ficient adaptation scheme for adjusting the configura- tions of EC algorithms has become a significant and promising research topic in the EC research community. This paper intends to provide a comprehensive survey on this rapidly growing field. We present a classification of adaptive EC （AEC） algorithms from the perspective of how an adaptation scheme is designed, involving the adaptation objects, adaptation evidences, and adapta- tion methods. In particular, by analyzing tile popula- tion distribution characteristics of EC algorithms, we discuss why and how the evolutionary state information of EC can be estimated and utilized for designing ef- fective EC adaptation schemes. Two AEC algorithms using the idea of evolutionary state estimation, includ- ing the clustering-based adaptive genetic algorithm and the adaptive particle swarm optimization algorithm are presented in detail. Some potential directions for the re- search of AECs are also discussed in this paper.

Time Complexity of Evolutionary Algorithms for Combinatorial Optimization:A Decade of Results

Computational time complexity analyzes of evolutionary algorithms （EAs） have been performed since the mid-nineties. The first results were related to very simple algorithms, such as the （1＋1）-EA, on toy problems. These efforts produced a deeper understanding of how EAs perform on different kinds of fitness landscapes and general mathematical tools that may be extended to the analysis of more complicated EAs on more realistic problems. In fact, in recent years, it has been possible to analyze the （1＋1）-EA on combinatorial optimization problems with practical applications and more realistic population-based EAs on structured toy problems. This paper presents a survey of the results obtained in the last decade along these two research lines. The most common mathematical techniques are introduced, the basic ideas behind them are discussed and their elective applications are highlighted. Solved problems that were still open are enumerated as are those still awaiting for a solution. New questions and problems arisen in the meantime are also considered.

Evolutionary Computation for Large-scale Multi-objective Optimization: A Decade of Progresses

Large-scale multi-objective optimization problems(MOPs)that involve a large number of decision variables,have emerged from many real-world applications.While evolutionary algorithms(EAs)have been widely acknowledged as a mainstream method for MOPs,most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables.More recently,it has been reported that traditional multi-objective EAs(MOEAs)suffer severe deterioration with the increase of decision variables.As a result,and motivated by the emergence of real-world large-scale MOPs,investigation of MOEAs in this aspect has attracted much more attention in the past decade.This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles.From the key difficulties of the large-scale MOPs,the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables.From the perspective of methodology,the large-scale MOEAs are categorized into three classes and introduced respectively:divide and conquer based,dimensionality reduction based and enhanced search-based approaches.Several future research directions are also discussed.

Evolutionary Computation for Expensive Optimization:A Survey

Expensive optimization problem(EOP) widely exists in various significant real-world applications. However, EOP requires expensive or even unaffordable costs for evaluating candidate solutions, which is expensive for the algorithm to find a satisfactory solution. Moreover, due to the fast-growing application demands in the economy and society, such as the emergence of the smart cities, the internet of things, and the big data era, solving EOP more efficiently has become increasingly essential in various fields, which poses great challenges on the problem-solving ability of optimization approach for EOP. Among various optimization approaches, evolutionary computation(EC) is a promising global optimization tool widely used for solving EOP efficiently in the past decades. Given the fruitful advancements of EC for EOP, it is essential to review these advancements in order to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey to show why and how EC can solve EOP efficiently. For this aim, this paper firstly analyzes the total optimization cost of EC in solving EOP. Then, based on the analysis, three promising research directions are pointed out for solving EOP, which are problem approximation and substitution, algorithm design and enhancement, and parallel and distributed computation. Note that, to the best of our knowledge, this paper is the first that outlines the possible directions for efficiently solving EOP by analyzing the total expensive cost. Based on this, existing works are reviewed comprehensively via a taxonomy with four parts, including the above three research directions and the real-world application part. Moreover, some future research directions are also discussed in this paper. It is believed that such a survey can attract attention, encourage discussions, and stimulate new EC research ideas for solving EOP and related real-world applications more efficiently.

Gait Optimization of a Quadruped Robot Using Evolutionary Computation

Evolutionary Computation(EC)has strengths in terms of computation for gait optimization.However,conventional evolutionary algorithms use typical gait parameters such as step length and swing height,which limit the trajectory deformation for optimization of the foot trajectory.Furthermore,the quantitative index of fitness convergence is insufficient.In this paper,we perform gait optimization of a quadruped robot using foot placement perturbation based on EC.The proposed algorithm has an atypical solution search range,which is generated by independent manipulation of each placement that forms the foot trajectory.A convergence index is also introduced to prevent premature cessation of learning.The conventional algorithm and the proposed algorithm are applied to a quadruped robot;walking performances are then compared by gait simulation.Although the two algorithms exhibit similar computation rates,the proposed algorithm shows better fitness and a wider search range.The evolutionary tendency of the walking trajectory is analyzed using the optimized results,and the findings provide insight into reliable leg trajectory design.

Evolutionary Computation in Social Propagation over Complex Networks: A Survey

Social propagation denotes the spread phenomena directly correlated to the human world and society, which includes but is not limited to the diffusion of human epidemics, human-made malicious viruses, fake news, social innovation, viral marketing, etc. Simulation and optimization are two major themes in social propagation, where network-based simulation helps to analyze and understand the social contagion, and problem-oriented optimization is devoted to contain or improve the infection results. Though there have been many models and optimization techniques, the matter of concern is that the increasing complexity and scales of propagation processes continuously refresh the former conclusions. Recently, evolutionary computation(EC) shows its potential in alleviating the concerns by introducing an evolving and developing perspective. With this insight, this paper intends to develop a comprehensive view of how EC takes effect in social propagation. Taxonomy is provided for classifying the propagation problems, and the applications of EC in solving these problems are reviewed. Furthermore, some open issues of social propagation and the potential applications of EC are discussed.This paper contributes to recognizing the problems in application-oriented EC design and paves the way for the development of evolving propagation dynamics.

Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization problems

A new method to solve dynamic nonlinear constrained optimization problems （DNCOP） is proposed. First, the time （environment） variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem （SNCOP）. Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into a static bi-objective optimization problem. As a result, the original DNCOP is approximately transformed into several static bi-objective optimization problems. Third, a new multiobjective evolutionary algorithm is proposed based on a new selection operator and an improved nonuniformity mutation operator. The simulation results indicate that the proposed algorithm is effective for DNCOP.

A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking

Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare-to front, retain the diversity of the population, and use less time.