Quantum seeded evolutionary computational technique for constrained optimization in engineering design and manufacturing

In this paper an attempt is made to develop a new Quantum Seeded Hybrid Evolutionary Computational Technique (QSHECT) that is general, flexible and efficient in solving single objective constrained optimization problems. It generates initial parents using quantum seeds. It is here that QSHECT incorporates ideas from the principles of quantum computation and integrates them in the current framework of Real Coded Evolutionary Algorithm (RCEA). It also incorporates Simulated Annealing (SA) in the selection process of Evolutionary Algorithm (EA) for child generation. The proposed algorithm has been tested on standard test problems and engineering design problems taken from the literature. In order to test this algorithm on domain-specific manufacturing problems, Neuro-Fuzzy (NF) modeling of hot extrusion is attempted and the NF model is incorporated as a fitness evaluator inside the QSHECT to form a new variant of this technique, i.e. Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Computational Technique (QSNFHECT) and is effectively applied for process optimization of hot extrusion process. The neuro-fuzzy model (NF) is also compared with statistical regression analysis (RA) model for evaluating the extrusion load. The NF model was found to be much superior. The optimal process parameters obtained by Quantum Seeded Neuro Fuzzy Hybrid Evolutionary Technique (QSNFHECT) are validated by the finite element model. The proposed methodology using QSNFHECT is a step towards meeting the challenges posed in intelligent manufacturing systems and opens new avenues for parameter estimation and optimization and can be easily incorporated in existing manufacturing setup.     

Optimal design of laser solid freeform fabrication system and real-time prediction of melt pool geometry using intelligent evolutionary algorithms

With the rapid growth of laser applications and the introduction of high efficiency lasers (e.g. fiber lasers), laser material processing has gained increasing importance in a variety of industries. Among the applications of laser technology, laser cladding has received significant attention due to its high potential for material processing such as metallic coating, high value component repair, prototyping, and even low-volume manufacturing. In this paper, two optimization methods have been applied to obtain optimal operating parameters of Laser Solid Freeform Fabrication Process (LSFF) as a real world engineering problem. First, Particle Swarm Optimization (PSO) algorithm was implemented for real-time prediction of melt pool geometry. Then, a hybrid evolutionary algorithm called Self-organizing Pareto based Evolutionary Algorithm (SOPEA) was proposed to find the optimal process parameters. For further assurance on the performance of the proposed optimization technique, it was compared to some well-known vector optimization algorithms such as Non-dominated Sorting Genetic Algorithm (NSGA-II) and Strength Pareto Evolutionary Algorithm (SPEA 2). Thereafter, it was applied for simultaneous optimization of clad height and melt pool depth in LSFF process. Since there is no exact mathematical model for the clad height (deposited layer thickness) and the melt pool depth, the authors developed two Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to estimate these two process parameters. Optimization procedure being done, the archived non-dominated solutions were surveyed to find the appropriate ranges of process parameters with acceptable dilutions. Finally, the selected optimal ranges were used to find a case with the minimum rapid prototyping time. The results indicate the acceptable potential of evolutionary strategies for controlling and optimization of LSFF process as a complicated engineering problem.     

Using throughput profit for selecting manufacturing process plan

In a task of process plan selection using workstations, manufacturing process plans have different precedence relationships, unequal operating times, and so on. It is necessary to consider these factors in evaluation models. Due to the use of different machines and tools that would increase the line efficiency with additional cost, a traditional total cost is not a good measure for the selection of manufacturing process plans. Hence, an evaluation model using throughput profit for each manufacturing process plan is proposed. In the first step of the proposed method, the precedence relationships of the manufacturing process for each part are decomposed. Several feasible combinations with different numbers of workstations and different task assignments are generated using a line balancing method. Then, an optimization model with associated throughput profit for process parameters is used for choosing the manufacturing process plans. Implementation examples are presented to illustrate this proposed procedure.     

Multileveled Symbiotic Evolutionary Algorithm: Application to FMS Loading Problems

Recently, there has been an increasing effort to address integrated problems that are composed of multiple interrelated sub-problems. Many integrated problems in the real world have a multileveled structure. This paper proposes a new method of solving integrated and multileveled problems. The proposed method is named Multileveled Symbiotic Evolutionary Algorithm (MSEA). MSEA is an evolutionary algorithm that imitates the process of symbiotic evolution, including endosymbiotic evolution. It is designed to promote the balance of population diversity and population convergence. To verify its applicability, MSEA is applied to loading problems of flexible manufacturing systems with various flexibilities. Through computer experiments, the features of MSEA are shown and their effects on search capability are discussed. The proposed algorithm is also compared with existing ones in terms of solution quality. The experimental results confirm the effectiveness of our approach.     

Solving a tri-objective supply chain problem with modified NSGA-II algorithm

In this paper, we propose the modification of an existing Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm has been applied on a tri-objective problem for a two echelon serial supply chain. The objectives considered are: (1) minimization of the total cost of a two-echelon serial supply chain and (2) minimization of the variance of order quantity and (3) minimization of the total inventory. The variance of order quantity is an important factor to consider since the variance of order quantity is used to measure the bullwhip effect which is one of the performance measures of a supply chain. The supply chain under consideration is assumed to consist of buyers and supplier. The production process at the supplier is an imperfect production process and thus produces defective items. A percentage of defective items are sold at a secondary market and the remaining defective items are repaired. We have introduced a mutation algorithm which has been embedded in the proposed algorithm. Since the proposed mutation algorithm is performed over the entire population, thus the mutation algorithm has caused the modification of the parts of the original NSGA-II. The results of the modified algorithm have been compared with those of the original NSGA-II and SPEA2 (Strength Pareto Evolutionary Algorithm 2) evolutionary algorithms for varying values of probability of crossover. The experimental results show that the proposed algorithm performs significantly better than the original NSGA-II and SPEA2.     

An innovative approach for resource sharing and scheduling in a sustainable distributed manufacturing system

Secure, transparent, and sustainable distributed manufacturing system (DMS) is a pressing need for current Industry 4.0. In this paper, exchange of highly sensitive information in a more transparent and secure way and to avoid the misunderstandings and trust issues between the enterprises a smart contract based on blockchain technology has been proposed in case of a distributed manufacturing environment. Here, we used a public-permission less Ethereum platform to execute the smart contracts in the Blockchain to process the customer orders and to identify the right enterprise. Later, a multi-objective mixed-integer linear programming (MILP) model is formulated for optimal resource sharing and scheduling in a considered sustainable DMS. The objectives of the proposed model consist of simultaneously improvement of the performance measures such as makespan, machine utilization, energy consumption, and reliability. To solve this MILP model, a new Multi-objective-based Hybridized Moth Flame Evolutionary Optimization Algorithm (HMFEO) is developed and then the effectiveness of the proposed algorithm is validated with the Non-dominated Sorting Genetic Algorithm (NSGA-III). The results obtained from implementing the model using experimental data along with different cases show the efficiency and the validity of the proposed model and solution approach. Moreover, several performance indicators like hyper volume are increased by nearly 15–20 % that shows the superiority of the proposed algorithm with the NSGA-III.     

Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms

In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang–Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks − five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.     

基于协同进化的约束多目标优化算法

针对约束多目标优化算法存在难以有效地兼顾收敛性和多样性的问题,提出一种基于协同进化的约束多目标优化算法.第一阶段,通过基于稳态演化的可行解搜索方式得到一个具有一定数量可行解的种群;第二阶段,将这个种群拆分为两个子种群,并通过双子种群协同进化的方式实现对收敛性和多样性的兼顾;最后采用标准约束多目标优化问题CF1～CF7、...     

Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill

Optimization algorithms are effective and powerful tools for solving the non-linear optimization problems. Backtracking Search Optimization Algorithm (BSA) is a newly proposed Evolutionary Algorithm (EA) and has been applied to optimize different complex optimization problems in science and engineering. In the present study, a new adaptive control parameter based Improved Backtracking Search Optimization Algorithm (IBSA) is suggested. Due to the validation of the suggested method, it has been applied to CEC2005 benchmark functions and the simulation results are compared with different existing algorithms. Also, it has been used to determine active earth pressure on retaining wall supporting c-Ф backfill using the pseudo dynamic method. Simulation result shows that the proposed method is suitable to solve such type of problems and the results obtained are found satisfactory.     

免疫进化混合猴王遗传算法

针对简单猴王遗传算法（MKGA）存在易陷入局部极值和稳定性较差的缺陷,提出了免疫进化混合猴王遗传算法（MKGAIEH）。MKGAIEH将总群体划分为若干个子群体,为了充分利用总群体中最优个体(总猴王)信息,引入免疫进化算法（IEA）对其进行免疫进化迭代计算;此外,对子群体内的其他个体,同时考虑子群体的子猴王与群体的总猴王对其进行交叉和变异遗传操作。当所有子群体的局部搜索完成后,再将各子群体的解重新混合。这种全局信息交换与子群内局部搜索相结合的策略不仅避免了早熟收敛,而且随着迭代的进行,还能以更高的精度逼近全局最优解。将MKGAIEH、MKGA、改进后的猴王遗传算法（IMKGA）、蜜蜂遗传算法（BEGA）、免疫进化粒子群蛙跳算法（IEPSOSFLA）和普通爬山算子遗传算法（COGA）对6个典型测试函数的计算结果进行了比较,其结果为:MKGAIEH对6个测试函数都能获得全局最优解,有5个测试函数获得的平均值和标准差比其他5种优化算法获得的平均值和标准差精度提高了几个数量级,达到了最小。这表明MKGAIEH具有更佳的寻优能力和更好的稳定性。     

共生进化算法母体选择策略性能研究及改进

轮盘赌在传统遗传算法中能加快进化速度和提高解质量,以共生进化算法求解一个复杂的柔性作业调度为例,跟踪共生种群进化过程。研究轮盘赌在以求得最优组合为目标的共生进化算法中对种群进化速度、种群多样性以及解质量的影响。为提高种群进化的解质量,引入了Worst策略。仿真实验表明,轮盘赌在共生进化算法中的应用不能促进解质量的提高,Worst策略能有效调节种群的进化速度并能提升解质量。     

Application of genetic algorithm to computer-aided process planning in distributed manufacturing environments

In a distributed manufacturing environment, factories possessing various machines and tools at different geographical locations are often combined to achieve the highest production efficiency. When jobs requiring several operations are received, feasible process plans are produced by those factories available. These process plans may vary due to different resource constraints. Therefore, obtaining an optimal or near-optimal process plan becomes important. This paper presents a genetic algorithm (GA), which, according to prescribed criteria such as minimizing processing time, could swiftly search for the optimal process plan for a single manufacturing system as well as distributed manufacturing systems. By applying the GA, the computer-aided process planning (CAPP) system can generate optimal or near-optimal process plans based on the criterion chosen. Case studies are included to demonstrate the feasibility and robustness of the approach. The main contribution of this work lies with the application of GA to CAPP in both a single and distributed manufacturing system. It is shown from the case study that the approach is comparative or better than the conventional single-factory CAPP.     

A simulated annealing algorithm approach to hybrid flow shop scheduling with sequence-dependent setup times

One of the scheduling problems with various applications in industries is hybrid flow shop. In hybrid flow shop, a series of n jobs are processed at a series of g workshops with several parallel machines in each workshop. To simplify the model construction in most research on hybrid flow shop scheduling problems, the setup times of operations have been ignored, combined with their corresponding processing times, or considered non sequence-dependent. However, in most real industries such as chemical, textile, metallurgical, printed circuit board, and automobile manufacturing, hybrid flow shop problems have sequence-dependent setup times (SDST). In this research, the problem of SDST hybrid flow shop scheduling with parallel identical machines to minimize the makespan is studied. A novel simulated annealing (NSA) algorithm is developed to produce a reasonable manufacturing schedule within an acceptable computational time. In this study, the proposed NSA uses a well combination of two moving operators for generating new solutions. The obtained results are compared with those computed by Random Key Genetic Algorithm (RKGA) and Immune Algorithm (IA) which are proposed previously. The results show that NSA outperforms both RKGA and IA.     

Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art-survey

Integrated manufacturing system (IMS) is a novel manufacturing environment which has been developed for the next generation of manufacturing and processing technologies. It consists of engineering design, process planning, manufacturing, quality management, and storage and retrieval functions. Improving the decision quality in those fields give rise to complex combinatorial optimization problems, unfortunately, most of them fall into the class of NP-hard problems. Find a satisfactory solution in an acceptable time play important roles. Evolutionary techniques (ET) have turned out to be potent methods to solve such kind of optimization problems. How to adapt evolutionary technique to the IMS is very challenging but frustrating. Many efforts have been made in order to give an efficient implementation of ET to optimize the specific problems in IMS.In this paper, we address four crucial issues in IMS, including design, planning, manufacturing, and distribution. Furthermore, some hot topics in these issues are selected to demonstrate the efficiency of ET’s application, such as layout design (LD) problem, flexible job-shop scheduling problem (fJSP), multistage process planning (MPP) problem, and advanced planning and scheduling (APS) problem. First, we formulate a generalized mathematic models for all those problems; several evolutionary algorithms which adapt to the problems have been proposed; some test instances based on the practical problems demonstrate the effectiveness and efficiency of our proposed approach.     

Real-time integrated production-scheduling and maintenance-planning in a flexible job shop with machine deterioration and condition-based maintenance

The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.     

多约束集群目标下武器-目标分配问题

面对未来作战中高密度、多方位的集群智能体,传统点对点饱和攻击已不是最佳策略,可通过选择合适的武器类型和作用点实现火力覆盖,达到武器数量小于目标数量的最大杀伤效果。综合考虑安全目标、毁伤门限、偏好指派等作战需求,首先,建立了多约束多目标武器-目标分配（CMWTA）数学模型;其次,设计了约束违反值的计算方法,并采用个体编码、检测修复和约束支配相结合的方式处理多约束;最后,设计了针对多目标武器-目标分配模型的收敛性度量指标,并基于多目标进化算法（MOEA）框架进行了仿真分析。其中在进化算法框架对比中,SPEA2下的Pareto集合容量主要分布于[21,25]区间内,NSGA-Ⅱ下的Pareto集合容量主要分布于[16,20],而MOEA/D下的Pareto集合容量均小于16;在修复算法验证中,修复算法将三种进化算法框架的Convergence指标提升了20%以上,且可将Pareto解集中不可行解的比例保持在0%。实验结果表明,在求解CMWTA模型中,SPEA2算法框架在分布性和收敛性上优于NSGA-Ⅱ和MOEA/D算法框架,且所提修复算法有效地提高了进化算法对非支配可行解的求解效率。     

基于混合演化算法的二维下料问题

介绍了将二维下料问题建模成旅行商问题的方法,指出单纯旅行商问题模型的局限性,认为其是一种启发式方法,理论上找不到全局最优解。针对于此,在原有模型的基础上引入旋转变换,提出了两种混合演化算法捆绑式深化算法和元算法。实例仿表明,该算法可以获得比普通经算法好的结果。     

基于多区域采样策略的混合粒子群优化求解多目标柔性作业车间调度问题

柔性作业车间调度问题（FJSP）是一类应用广泛的组合优化问题。针对多目标FJSP求解过程复杂、算法易陷入局部最优的问题,提出了一种基于多区域采样策略的混合粒子群优化算法（HPSO-MRS）,以同时优化最大完工时间和总机器延迟时间这两个目标。多区域采样策略能够区分粒子所在Pareto前沿面的位置,根据不同区域进行采样重组,并为采样后位于Pareto前沿面多个区域的粒子规划相应的运动方向,从而有针对性地调整粒子在多个方向上的收敛能力,并带来一定程度的均匀分布能力的提升。此外,编解码方面使用带插空机制的解码策略来消除可能存在的局部左移;粒子更新方面将传统粒子群优化（PSO）算法的粒子更新方式与遗传算法（GA）的交叉变异算子相结合,提升了算法搜索过程的多样性并避免算法陷入局部最优。把所提算法在Benchmark问题Mk01~Mk10上进行测试,与传统的HPSO、NSGA-Ⅱ、基于适应度分配策略的多目标进化算法（SPEA2）和基于分解的多目标进化算法（MOEA/D）进行算法效力和运行效率对比。显著性分析的实验结果表明,HPSO-MRS在收敛性评价指标HV和IGD上分别在85%和77.5%的对照组中显著优于对比算法,而该算法在35%的对照组中的分布性指标Spacing显著优于对比算法,且均不存在所提算法显著差于对比算法的情况。可见相较于对比算法,所提出的算法具备较好的收敛与分布性能。     

柔性作业调度的串并行模型对比与求解

作业处理中的柔性使得作业调度更为灵活,作业中操作的执行顺序满足拓扑排序是作业调度的前提。是否允许没有优先关系的操作在不同的机器上同时执行是区分串行和并行调度的条件。文中以共生进化算法求解一个复杂的作业调度模型为例,给出了算法实现串行调度和并行调度的具体区别,并给出了串行和并行调度的结果。结果表明,并行相对于串行对算法效率的提高与柔性大小相关,与作业的规模成反比。     

Multi-objective K-connected Deployment and Power Assignment in WSNs using a problem-specific constrained evolutionary algorithm based on decomposition

The K-connected Deployment and Power Assignment Problem (DPAP) in WSNs aims at deciding both the sensor locations and transmit power levels, for maximizing the network coverage and lifetime objectives under K-connectivity constraints, in a single run. Recently, it is shown that the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is a strong enough tool for dealing with unconstraint real life problems (such as DPAP), emphasizing the importance of incorporating problem-specific knowledge for increasing its efficiency. In a constrained Multi-objective Optimization Problem (such as K-connected DPAP), the search space is divided into feasible and infeasible regions. Therefore, problem-specific operators are designed for MOEA/D to direct the search into optimal, feasible regions of the space. Namely, a DPAP-specific population initialization that seeds the initial solutions into promising regions, problem-specific genetic operators (i.e. M-tournament selection, adaptive crossover and mutation) for generating good, feasible solutions and a DPAP-specific Repair Heuristic (RH) that transforms an infeasible solution into a feasible one and maintains the MOEA/D’s efficiency simultaneously. Simulation results have shown the importance of each proposed operator and their interrelation, as well as the superiority of the DPAP-specific MOEA/D against the popular constrained NSGA-II in several WSN instances.