A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning

A considerable growth in worldwide container transportation needs essential optimization of terminal operations. An operation schedule for berth and quay cranes can significantly affect turnaround time of ships, which is an important objective of all schedules in a port. This paper addresses the problem of determining the berthing position and time of each ship as well as the number of quay cranes assigned to each ship. The objective of the problem is to minimize the sum of the handling time, waiting time and the delay time for every ship. We introduce a formulation for the simultaneous berth and quay crane scheduling problem. Next, we combine genetic algorithm with heuristic to find an approximate solution for the problem. Computational experiments show that the proposed approaches are applicable to solve this difficult but essential terminal operation problem.     

A decomposition-based hybrid multiobjective evolutionary algorithm with dynamic resource allocation

Different crossover operators suit different problems. It is, therefore, potentially problematic to chose the ideal crossover operator in an evolutionary optimization scheme. Using multiple crossover operators could be an effective way to address this issue. This paper reports on the implementation of this idea, i.e. the use of two crossover operators in a decomposition-based multi-objective evolutionary algorithm, but not simultaneously. After each cycle, the operator which has helped produce the better offspring is rewarded. This means that the overall algorithm uses a dynamic resource allocation to reward the better of the crossover operators in the optimization process. The operators used are the Simplex Crossover operator (SPX) and the Center of Mass Crossover operator (CMX). We report experimental results that show that this innovative use of two crossover operators improves the algorithm performance on standard test problems. Results on the sensitivity of the suggested algorithm to key parameters such as population size, neighborhood size and maximum number of solutions to be altered for a given subproblem in the the decomposition process are also included.     

A two-level costate prediction algorithm for non-linear systems

A two level costate prediction algorithm is developed for the optimisation of non-linear discrete dynamical systems.The algorithm is proved to converge under fairly mild conditions. The algorithm appears to require substantially smaller computation time and storage than previous two level algorithms. The method is illustrated on a practical problem of optimisation of turbogenerator transient performance.     

A hybrid evolutionary algorithm for distribution feeder reconfiguration

This paper presents a new method to reduce the distribution system loss by feeder reconfiguration.This new method combines self-adaptive particle swarm optimization(SAPSO) with shuffled frog-leaping algorithm(SFLA) in an attempt to find the global optimal solutions for the distribution feeder reconfiguration(DFR).In PSO algorithm,appropriate adjustment of the parameters is cumbersome and usually requires a lot of time and effort.Thus,a self-adaptive framework is proposed to improve the robustness of PSO.In ...     

A multi-objective evolutionary algorithm guided by directed search for dynamic scheduling

This paper considers dynamic multi-objective machine scheduling problems in response to continuous arrival of new jobs, under the assumption that jobs can be rejected and job processing time is controllable. The operational cost and the cost of deviation from the baseline schedule need to be optimized simultaneously. To solve these dynamic scheduling problems, a directed search strategy (DSS) is introduced into the elitist non-dominated sorting genetic algorithm (NSGA-II) to enhance its capability of tracking changing optimums while maintaining fast convergence. The DSS consists of a population re-initialization mechanism (PRM) to be adopted upon the arrival of new jobs and an offspring generation mechanism (OGM) during evolutionary optimization. PRM re-initializes the population by repairing the non-dominated solutions obtained before the disturbances occur, modifying randomly generated solutions according to the structural properties, as well as randomly generating solutions. OGM generates offspring individuals by fine-tuning a few randomly selected individuals in the parent population, employing intermediate crossover in combination with Gaussian mutations to generate offspring, and using intermediate crossover together with a differential evolution based mutation operator. Both PRM and OGM aim to strike a good balance between exploration and exploitation in solving the dynamic multi-objective scheduling problem. Comparative studies are performed on a variety of problem instances of different sizes and with different changing dynamics. Experimental results demonstrate that the proposed DSS is effective in handling the dynamic scheduling problems under investigation.     

A hybrid algorithm for queueing systems

Abstract In this paper, we propose a hybrid algorithm based on [12] for solving linear systems of equations. The hybrid algorithm combines the evolutionary algorithm and the successive over-relaxation (SOR) method. The evolutionary algorithm allows the relaxation parameter w to be adaptive in the SOR method. We prove the convergence of the hybrid algorithm for strictly diagonal dominant linear systems. We then apply it to solve the steady-state probability distributions of Markovian queueing systems. Numerical examples are given to demonstrate the fast convergence rate of the method.     

A parallel evolutionary algorithm to optimize dynamic data types in embedded systems

New multimedia embedded applications are increasingly dynamic, and rely on dynamically-allocated data types (DDTs) to store their data. The optimization of DDTs for each target embedded system is a time-consuming process due to the large searching space of possible DDTs implementations. That implies the minimization of embedded design variables (memory accesses, power consumption and memory usage). Up to know, some very effective heuristic algorithms have been developed in order to solve this problem, but it is unknown how good the selected DDTs are since the problem is NP-complete and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time, but also to implement new algorithms. This paper describes several parallel evolutionary algorithms for DDTs optimization in Embedded Systems, where parallelism improves the solutions found by the corresponding sequential algorithm, which indeed is quite effective compared with other previously proposed procedures. Experimental results show how a novel parallel multi-objective genetic algorithm, which combines NSGA-II and SPEA2, allows designers to reach a larger number of solutions than previous approximations.     

A parallel evolutionary algorithm to optimize dynamic memory managers in embedded systems

For the last 30 years, several dynamic memory managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players, signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time-consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40× when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two well-known general-purpose DMMs and two state-of-the-art optimization methodologies.     

一种求解双目标job shop问题的混合进化算法

提出一种求解双目标job shop排序问题的混合进化算法．该算法采用改进的精英复制策略,降低了计算复杂性;通过引入递进进化模式,避免了算法的早熟;通过递进过程中的非劣解邻域搜索,增强了算法局部搜索性能．采用该算法和代表性算法NSGA-Ⅱ,MOGLS对82个标准双目标job shop算例进行优化对比,所得结果验证了该算法求解双目标job shop排序问题的有效性．     

A hybrid evolutionary algorithm for the resource-constrained project scheduling problem

The resource-constrained project scheduling problem (RCPSP) is an NP-hard optimization problem. RCPSP is one of the most important and challenging problems in the project management field. In the past few years, many researches have been proposed for solving the RCPSP. The objective of this problem is to schedule the activities under limited resources so that the project makespan is minimized. This paper proposes a new algorithm for solving RCPSP that combines the concepts of negative selection mechanism of the biologic immune system, simulated annealing algorithm (SA), tabu search algorithm (TS) and genetic algorithm (GA) together. The performance of the proposed algorithm is evaluated and compared to current state-of-the-art metaheuristic algorithms. In this study, the benchmark data sets used in testing the performance of the proposed algorithm are obtained from the project scheduling problem library. The performance is measured in terms of the average percentage deviation from the critical path lower bound. The experimental results show that the proposed algorithm outperforms the state-of-the-art metaheuristic algorithms on all standard benchmark data sets.     

A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding

DNA encoding is crucial to successful DNA computation, which has been extensively researched in recent years. It is difficult to solve by the traditional optimization methods for DNA encoding as it has to meet simultaneously several constraints, such as physical, chemical and logical constraints. In this paper, a novel quantum chaotic swarm evolutionary algorithm (QCSEA) is presented, and is first used to solve the DNA sequence optimization problem. By merging the particle swarm optimization and the chaotic search, the hybrid algorithm cannot only avoid the disadvantage of easily getting to the local optional solution in the later evolution period, but also keeps the rapid convergence performance. The simulation results demonstrate that the proposed quantum chaotic swarm evolutionary algorithm is valid and outperforms the genetic algorithm and conventional evolutionary algorithm for DNA encoding.     

A hybrid multi-objective evolutionary algorithm with feedback mechanism

Exploration and exploitation are two cornerstones for multi-objective evolutionary algorithms (MOEAs). To balance exploration and exploitation, we propose an efficient hybrid MOEA (i.e., MOHGD) by integrating multiple techniques and feedback mechanism. Multiple techniques include harmony search, genetic operator and differential evolution, which can improve the search diversity. Whereas hybrid selection mechanism contributes to the search efficiency by integrating the advantages of the static and adaptive selection scheme. Therefore, multiple techniques based on the hybrid selection strategy can effectively enhance the exploration ability of the MOHGD. Besides, we propose a feedback strategy to transfer some non-dominated solutions from the external archive to the parent population. This feedback strategy can strengthen convergence toward Pareto optimal solutions and improve the exploitation ability of the MOHGD. The proposed MOHGD has been evaluated on benchmarks against other state of the art MOEAs in terms of convergence, spread, coverage, and convergence speed. Computational results show that the proposed MOHGD is competitive or superior to other MOEAs considered in this paper.     

金字塔双层动态规划立体匹配算法

针对控制点修正的动态规划立体匹配算法存在控制点求取时阀长、实时性差的问题．提出一种金字塔双层动态规划立体匹配算法．采用金字塔算法求取低、商分辨率图像,然后分别在低、商分辨宰图像上求取候选控制点集和最终控制点集,并用最终控制点集修正商分辨率图像上的动态规划立体匹配．由干候选控制点集的求取在低分辨率图像上进行,算法用时大为减少．实验证明,此算法匹配率商、速度快．     

A dynamic surrogate-assisted evolutionary algorithm framework for expensive structural optimization

Structural and Multidisciplinary Optimization - In the expensive structural optimization, the data-driven surrogate model has been proven to be an effective alternative to physical simulation (or...     

求解动态优化问题的自组织进化算法

为在环境发生变化后跟踪最优解的变化,提出一种自组织单变量边缘分布算法(SOUMDA)来求解动态优化问题.自组织策略包含扩散和惯性速度模型,扩散模型利用当前环境的局部信息使群体向外扩散,惯性速度模型利用最优解的历史信息进行预测.将自组织策略与单变量边缘分布算法(UMDA)结合,使得算法在环境变化后自适应地增加种群多样性,提高算法适应能力,快速跟踪最优解.利用动态sphere函数对所提出的算法进行测试,并与UMDA和MUMDA算法进行比较,结果表明所设计的算法能快速适应环境的变化,跟踪最优解.     

A tabu search based hybrid evolutionary algorithm for the max-cut problem

This paper presents a tabu search based hybrid evolutionary algorithm (TSHEA) for solving the max-cut problem. The proposed algorithm integrates a distance-and-quality based solution combination operator and a tabu search procedure based on neighborhood combination of one-flip and constrained exchange moves. Comparisons with leading reference algorithms from the literature disclose that the proposed algorithm discovers new best solutions for 15 out of 91 instances, while matching the best known solutions on all but 4 instances. Analysis indicates that the neighborhood combination and the solution combination operator play key roles to the effectiveness of the proposed algorithm.     

多群体混合进化算法求解IPPS问题

针对工艺规划与调度集成（Integration of Process Planning and Scheduling, IPPS）问题求解复杂性,为提高求解效率,设计了包含探索种群,寻优种群和最优种群的多群体混合进化算法,通过运用混合遗传算法和基于聚类淘汰机制的差分进化算法分别更新探索种群中工艺链和加工顺序链,保持可行解多样性和差异性。然后利用克隆领域搜索算法完成寻优种群中可行解的克隆和领域搜索,进一步提高种群质量。最后按照精英保留策略更新最优种群获得全局最优解。并通过实例计算对比,结果显示算法搜索效率和求解质量均有明显改善,且稳定性较好,表明该算法求解IPPS问题的可行性及优越性。     

求解边值固定动态优化问题的一种混合算法

针对边值固定动态优化问题的数值求解,提出了一种集成随机性方法与确定性方法的一种新的混合算法。迭代遗传算法(IGA)把初始种群及繁衍产生的后代不断供给两点梯度法,两点梯度法以其为初值搜索满足边值固定约束的可行控制策略并回送给迭代遗传算法,迭代遗传算法则根据可行控制策略对应的目标函数值进行选择与进化操作。该混合算法简便易行。实例研究显示了该混合算法的可行性与稳健性,能以足够的精度满足边值约束。