基于离散差分进化算法雌多维0／1背包问题求解

多维0／1背包问题（MKP）是一种典型的组合优化问题,并且被广泛的应用于各种工程领域。差分进化算法（DE）是一种有效的进化算法,能处理各种复杂的非线性优化问题,但主要是用来解决连续领域的优化问题。提出一种离散差分进化算法,并用来求解MKP问题。在经典测试集上的实验结果表明,提出的算法能更快的求得最优解。     

考虑碳排放及时间窗的LIRP模型与算法研究

随着城市内车辆的不断增加,汽车尾气的排放对环境的影响越来越大,但传统的LIRP研究很少考虑节能减排的因素。本文在传统的LIRP基础上,针对考虑碳排放成本和时间窗惩罚成本的城市车辆配送问题,将选址、库存和路径集成化进行研究,建立以经济成本最小化为目标的LIRP模型,采用差分进化算法对该问题进行求解。通过实验,验证设计的差分进化算法在求解该问题时的有效性。     

基于TSP的改进差分进化算法

针对TSP问题,提出一种改进的差分进化算法:利用贪心算法产生初始种群,定义特有的编码匹配函数进行变异操作,排序法修复变异个体,并采用顺序交叉,在变异操作之后,加入新的选择机制,防止交叉操作破坏变异出的优良个体,实验结果表明改进后的差分进化算法能够高效地解决TSP问题,体现良好的优化性能。     

基于差分进化算法的供水系统节能调度研究

针对当前供水系统节能降耗的需求,根据供水系统运行特点,利用合理划分调度期的方式,建立了以节能为目标的优化调度模型;采用差分进化算法对模型进行求解,应用实数编码将决策变量表示为进化种群中的个体;运用罚函数法对约束条件进行转换,并使用加法和乘法的形式进行组合,构建了适应度函数;采用标准差分策略进行变异操作;以天津市中心城区供水系统为例,验证了模型与差分进化算法的有效性,并与遗传算法进行了对比,显示出前者在求解该模型上具有一定的优势。     

基于扰动抑制的差分进化算法的纸浆浓度控制系统

目的为了解决传统单PID控制优化算法仅关注跟踪性能,无法满足在纸浆浓度控制系统等工业生产环节中控制品质和成品良率的需求。方法将PID控制系统离散化以计算在高斯扰动下的输出方差作为扰动抑制的依据,并结合ITAE指标通过差分进化算法优化PID性能。结果仿真表明,该整定方法可以通过权值选取优化的偏好,自由调节PID控制器的性能表现,对比基于Z-N法的PID控制和基于PSO算法优化的PID控制,基于扰动抑制差分进化算法的PID控制的ITAE指标为14.3495,输出方差为31.8530,均优于其他2种算法。结论基于扰动抑制的差分进化算法可以通过用户自定义权重来协调纸浆浓度的输出方差和跟踪性能,从更实际的角度整定纸浆浓度控制系统的PID控制器参数,使得控制系统的性能指标满足工业生产要求。     

高斯变异差分进化算法及其应用研究

为平衡算法收敛速度和全局搜索能力,克服差分进化算法易“早熟”的缺陷,在分析引起种群多样性下降及个体进化停滞原因的基础上,通过引入高斯变异操作,提出了基于高斯变异改进的差分进化算法(modified differential evolution base on Gauss mutation, GMDE). 数值仿真及2个工程优化问题的求解结果表明本文算法能有效避免“早熟”收敛,且在算法收敛速度和全局搜索能力上取得了较好的平衡.     

改进差分进化算法及啤酒灌装机液位控制PID参数整定

目的 针对啤酒液位控制系统存在PID参数整定难、非线性、滞后性问题,提出一种改进基于邻域的改进差分进化算法,应用于PID参数优化整定中,从而提高灌装机的工作效率和啤酒的质量。方法 文中对差分进化算法进行改进,设计一种新型的变异策略,在变异环节引入邻域搜索操作;根据当前种群的分布情况,实时对邻域的个数进行自适应分配,以提升算法全局和局部搜索能力;与2种基本差分进化算法和4种改进差分进化算法对比,用18个测试函数验证文中所提出算法的性能。结果 仿真结果表明,相较于基本差分进化算法,使用改进的差分进化算法整定的PID参数,调节时间减少0.22 s,上升时间减少0.04 s,超调量降低7.63%。结论 通过改进的差分进化算法对啤酒灌装机液位PID参数的优化整定,可以显著改善控制系统的超调量、上升时间和稳态误差等性能,实现了液位的稳定控制。     

自适应人工蜂群优化极限学习机在拉曼光谱血液定量分析中的应用

提出了一种基于自适应差分进化人工蜂群优化极限学习机预测血液各组分浓度的方法。首先应用人工蜂群算法对输入权值和隐含层阈值迭代寻优;其次结合差分进化进一步提高模型精度且避免后期易陷入局部最优等问题;由于差分进化算法交叉率和变异率存在凭经验给定的不确定性,最后引入了自适应调整的思想提出自适应差分进化人工蜂群算法优化极限学习机算法的模型,将其应用于血液成分定量分析中。实验表明,自适应差分进化人工蜂群算法优化的极限学习机模型具有较高的预测精度,模型具有较强的稳健性。     

基于智能优化算法的膜系设计展望

本文概述了膜系设计的主流设计方法。介绍了粒了群优化算法和差分进化算法两种新兴智能优化算法,阐述了这两种新的智能优化算法的算法原理。提出未来将智粒子群优化算法和差分进化算法引入膜系设计的研究方向展望。     

模糊环境下基于遗传差分协同进化的多阶段投资组合模型

现实经济活动中投资一般是不确定的和随机的,投资者对于风险资产的选择大多情况下是多阶段的。基于该现实因素,在模糊环境下考虑多个摩擦因素,利用交易限制引入资产的基数约束,建立可能性均值–下半方差–熵多阶段投资组合优化模型(V-S-M),该模型是一个多阶段混合整数规划问题。同时,给出了求解该模型的一个遗传差分协同进化算法(GAHDE),并对不同风险态度下的投资组合策略进行了分析,同时将所得数值结果与可能性均值–下半方差模型(V-M)和可能性均值–熵模型(S-M)进行模型对比,与标准的遗传算法和差分进化算法进行了算法对比,结果验证了所建模型和设计算法的优越性与有效性。     

Comparison of Dynamic Differential Evolution and Self-Adaptive Dynamic Differential Evolution for Buried Metallic Cylinder

The application of two techniques for the reconstruction of shape reconstruction of a metallic cylinder from scattered field measurements is studied in this paper. These techniques are applied to two-dimensional configurations, for which the method of moment (MoM) is applied to solve the integral equations. Considering that the microwave imaging is recast as a nonlinear optimization problem, an objective function is defined by the norm of the difference between the measured scattered electric fields and those calculated for each estimated metallic cylinder. Thus, the shape of a metallic cylinder can be obtained by minimizing the objective function. In order to solve this inverse scattering problem, two techniques are employed. The first one is based on dynamic differential evolution (DDE) algorithm, while the second one is an improved version of the DDE algorithm with self-adaptive control parameters, called SADDE. Both techniques are tested for the simulated data contaminated by additive white Gaussian noise. Numerical results indicate that SADDE algorithm outperforms DDE algorithm in terms of reconstruction accuracy and convergence speed.     

A re-entrant hybrid flow shop scheduling problem with machine eligibility constraints

A production scheduling problem originating from a real rotor workshop is addressed in the paper. Given its specific characteristics, the problem is formulated as a re-entrant hybrid flow shop scheduling problem with machine eligibility constraints. A mixed integer linear programming model of the problem is provided and solved by the Cplex solver. In order to solve larger sized problems, a discrete differential evolution (DDE) algorithm with a modified crossover operator is proposed. More importantly, a new decoder addressing the machine eligibility constraints is developed and embedded to the algorithm. To validate the performance of the proposed DDE algorithm, various test problems are examined. The efficiency of the proposed algorithm is compared with two other algorithms modified from the existing ones in the literatures. A one-way ANOVA analysis and a sensitivity analysis are applied to intensify the superiority of the new decoder. Tightness of due dates and different levels of scarcity of machines subject to machine eligibility restrictions are discussed in the sensitivity analysis. The results indicate the pre-eminence of the new decoder and the proposed DDE algorithm.     

Optimal reservoir flood operation using a decomposition-based multi-objective evolutionary algorithm

Reservoir flood control operation (RFCO) is a challenging optimization problem with interdependent decision variables and multiple conflicting criteria. By considering safety both upstream and downstream of the dam, a multi-objective optimization model is built for RFCO. To solve this problem, a multi-objective optimizer, the multi-objective evolutionary algorithm based on decomposition–differential evolution (MOEA/D-DE), is developed by introducing a differential evolution-inspired recombination into the algorithmic framework of the decomposition-based multi-objective optimization algorithm, which has been proven to be effective for solving complex multi-objective optimization problems. Experimental results on four typical floods at the Ankang reservoir illustrated that the suggested algorithm outperforms or performs as well as the comparison algorithms. It can significantly reduce the flood peak and also guarantee the dam’s safety.     

Chemical process dynamic optimization based on the differential evolution algorithm with an adaptive scheduling mutation strategy

To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self-adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process. The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem.     

Modified differential evolution algorithm for simple assembly line balancing with a limit on the number of machine types

This article proposes the differential evolution algorithm (DE) and the modified differential evolution algorithm (DE-C) to solve a simple assembly line balancing problem type 1 (SALBP-1) and SALBP-1 when the maximum number of machine types in a workstation is considered (SALBP-1M). The proposed algorithms are tested and compared with existing effective heuristics using various sets of test instances found in the literature. The computational results show that the proposed heuristics is one of the best methods, compared with the other approaches.     

Modified artificial bee colony optimization with block perturbation strategy

As a newly emerged swarm intelligence-based optimizer, the artificial bee colony (ABC) algorithm has attracted the interest of researchers in recent years owing to its ease of use and efficiency. In this article, a modified ABC algorithm with block perturbation strategy (BABC) is proposed. Unlike basic ABC, in the BABC algorithm, not one element but a block of elements from the parent solutions is changed while producing a new solution. The performance of the BABC algorithm is investigated and compared with that of the basic ABC, modified ABC, Brest's differential evolution, self-adaptive differential evolution and restart covariance matrix adaptation evolution strategy (IPOP-CMA-ES) over a set of widely used benchmark functions. The obtained results show that the performance of BABC is better than, or at least comparable to, that of the basic ABC, improved differential evolution variants and IPOP-CMA-ES in terms of convergence speed and final solution accuracy.     

Two enhanced differential evolution algorithms for job shop scheduling problems

This paper proposes two new differential evolution algorithms (DE) for solving the job shop scheduling problem (JSP) that minimises two single objective functions: makespan and total weighted tardiness. The proposed algorithms aim to enhance the efficiency of the search by dynamically balancing exploration and exploitation ability in DE and avoiding the problem of premature convergence. The first algorithm allows DE population to simultaneously perform different mutation strategies in order to extract the strengths of various strategies and compensate for the weaknesses of each individual strategy to enhance the overall performance. The second algorithm allows the whole DE population to change the search behaviour whenever the solutions do not improve. This study also introduces a modified local mutation operation embedded in the two proposed DE algorithms to promote exploitation in different areas of the search space. In addition, a local search technique, called Critical Block (CB) neighbourhood, is applied to enhance the quality of solutions. The performances of the proposed algorithms are evaluated on a set of benchmark problems and compared with results obtained from an efficient existing Particle Swarm Optimisation (PSO) algorithm. The numerical results demonstrate that the proposed DE algorithms yield promising results while using shorter computing times and fewer numbers of function evaluations.     

A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems

Job shop scheduling problem (JSSP) is a typical NP-hard problem. In order to improve the solving efficiency for JSSP, a hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search is proposed in this paper, which combines the merits of Estimation of distribution algorithm and Differential evolution (DE). Meanwhile, to strengthen the searching ability of the proposed algorithm, a chaotic strategy is introduced to update the parameters of DE. Two mutation operators are adopted. A neighbourhood search (NS) algorithm based on blocks on critical path is used to further improve the solution quality. Finally, the parametric sensitivity of the proposed algorithm has been analysed based on the Taguchi method of design of experiment. The proposed algorithm was tested through a set of typical benchmark problems of JSSP. The results demonstrated the effectiveness of the proposed algorithm for solving JSSP.     

Reliability reallocation for cost uncertainty of fuel cell vehicles via improved differential evolution algorithm

A fuel cell vehicle (FCV) is composed of many subsystems; it is necessary to reallocate subsystem reliability to improve FCV system reliability. A comprehensive evaluation method considering uncertainty is proposed to obtain the interval value of feasibility factor. The FCV cost uncertainty model is established on the basic of parameters such as feasibility factor, initial reliability, limit reliability, and subsystem cost. To solve the optimization problem for cost uncertainty, the cost interval model is transformed into a deterministic model by interval order relation. An improved differential evolution (DE) algorithm is proposed to reallocate subsystem reliability for minimum cost.