An asymmetric multileveled symbiotic evolutionary algorithm for integrated FMS scheduling

This paper considers the integrated FMS (flexible manufacturing system) scheduling problem (IFSP) consisting of loading, routing, and sequencing subproblems that are interrelated to each other. In scheduling FMS, the decisions for the subproblems should be appropriately made to improve resource utilization. It is also important to fully exploit the potential of the inherent flexibility of FMS. In this paper, a symbiotic evolutionary algorithm, named asymmetric multileveled symbiotic evolutionary algorithm (AMSEA), is proposed to solve the IFSP. AMSEA imitates the natural process of symbiotic evolution and endosymbiotic evolution. Genetic representations and operators suitable for the subproblems are proposed. A neighborhood-based coevolutionary strategy is employed to maintain the population diversity. AMSEA has the strength to simultaneously solve subproblems for loading, routing, and sequencing and to easily handle a variety of FMS flexibilities. The extensive experiments are carried out to verify the performance of AMSEA, and the results are reported.     

Multi-objective evolutionary algorithms based on the summation of normalized objectives and diversified selection

Most multi-objective evolutionary algorithms (MOEAs) use the concept of dominance in the search process to select the top solutions as parents in an elitist manner. However, as MOEAs are probabilistic search methods, some useful information may be wasted, if the dominated solutions are completely disregarded. In addition, the diversity may be lost during the early stages of the search process leading to a locally optimal or partial Pareto-front. Beside this, the non-domination sorting process is complex and time consuming. To overcome these problems, this paper proposes multi-objective evolutionary algorithms based on Summation of normalized objective values and diversified selection (SNOV-DS). The performance of this algorithm is tested on a set of benchmark problems using both multi-objective evolutionary programming (MOEP) and multi-objective differential evolution (MODE). With the proposed method, the performance metric has improved significantly and the speed of the parent selection process has also increased when compared with the non-domination sorting. In addition, the proposed algorithm also outperforms ten other algorithms.     

Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.     

Global path planning of mobile robots using a memetic algorithm

In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.     

Multi-objective genetic local search algorithm using Kohonen’s neural map

Genetic Algorithms (GAs) are population based global search methods that can escape from local optima traps and find the global optima regions. However, near the optimum set their intensification process is often inaccurate. This is because the search strategy of GAs is completely probabilistic. With a random search near the optimum sets, there is a small probability to improve current solution. Another drawback of the GAs is genetic drift. The GAs search process is a black box process and no one knows that which region is being searched by the algorithm and it is possible that GAs search only a small region in the feasible space. On the other hand, GAs usually do not use the existing information about the optimality regions in past iterations.In this paper, a new method called SOM-Based Multi-Objective GA (SBMOGA) is proposed to improve the genetic diversity. In SBMOGA, a grid of neurons use the concept of learning rule of Self-Organizing Map (SOM) supporting by Variable Neighborhood Search (VNS) learn from genetic algorithm improving both local and global search. SOM is a neural network which is capable of learning and can improve the efficiency of data processing algorithms. The VNS algorithm is developed to enhance the local search efficiency in the Evolutionary Algorithms (EAs). The SOM uses a multi-objective learning rule based-on Pareto dominance to train its neurons. The neurons gradually move toward better fitness areas in some trajectories in feasible space. The knowledge of optimum front in past generations is saved in form of trajectories. The final state of the neurons determines a set of new solutions that can be regarded as the probability density distribution function of the high fitness areas in the multi-objective space. The new set of solutions potentially can improve the GAs overall efficiency. In the last section of this paper, the applicability of the proposed algorithm is examined in developing optimal policies for a real world multi-objective multi-reservoir system which is a non-linear, non-convex, multi-objective optimization problem.     

Multi-objective supply planning for two-level assembly systems with stochastic lead times

This paper considers two-level assembly systems whose lead times of components are stochastic with known discrete random distributions. In such a system, supply planning requires determination of release dates of components at level 2 in order to minimize expected holding cost and to maximize customer service. Hnaien et al. [Hnaien F, Delorme X, Dolgui A. Multi-objective optimization for inventory control in two-level assembly systems under uncertainty of lead times. Computers and Operations Research 2010; 37:1835-43] have recently examined this problem, trying to solve it through multi-objective genetic algorithms. However, some reconsideration in their paper is unavoidable. The main problem with Hnaien et al. proposal is their wrong mathematical model. In addition, the proposed algorithms do not work properly in large-scale instances. In the current paper, this model is corrected and solved via a new approach based on NSGA-II that is called Guided NSGA-II. This approach tries to guide search toward preferable regions in the solution space. According to the statistical analyses, the guided NSGA-II has the higher performance in comparison with the basic NSGA-II used by Hnaien et al. Moreover, the wrongly reported characteristics of the Pareto front shape provided by Hnaien et al. are modified.     

Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty

Radio frequency identification (RFID) is widely used for item identification and tracking. Due to the limited communication range between readers and tags, how to configure a RFID system in a large area is important but challenging. To configure a RFID system, most existing results are based on cost minimization through using 0/1 identification model. In practice, the system is interfered by environment and probabilistic model would be more reliable. To make sure the quality of the system, more objectives, such as interference and coverage, should be considered in addition to cost. In this paper, we propose a probabilistic-based multi-objective optimization model to address these challenges. The objectives to be optimized include number of readers, interference level and coverage of tags. A decomposition-based firefly algorithm is designed to solve this multi-objective optimization problem. Virtual force is integrated into random walk to guide readers moving in order to enhance exploitation. Numerical simulations are introduced to demonstrate and validate our proposed method. Comparing with existing methods, such as Non-dominated Sorting Genetic Algorithm-II and Multi-objective Particle Swarm Optimization approaches, our proposed method can achieve better performance in terms of quality metric and generational distance under the same computational environment. However, the spacing metric of the proposed method is slightly inferior to those compared methods.     

PGA/MOEAD: a preference-guided evolutionary algorithm for multi-objective decision-making problems with interval-valued fuzzy preferences

In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.     

基于进化算法的优化平台设计

线性规划非线性规划等优化软件在社会、经济、工程等领域应用潜力巨大。现有优化软件大都采用的是经典的局部优化技术或者简单的全局优化技术。论文将进化算法引入称为优化平台的优化软件设计。对平台的关键技术进行了分析,提出了相应的平台方案,并予以了实现。该平台方案的特点是:界面动态调整增广目标函数中的惩罚因子,使用两个特别的进化算子,采用了特别的并行计算机制和退回机制。经测试,按所提方案实现的平台,操作方便,求解精度高而稳定,有显著的优越性。所提的优化平台方案是令人满意的。     

Integration of process planning and production scheduling based on genetic algorithm

Lacking of flexibility in the traditional workshop production, a genetic algorithm is proposed to implement the integration of process planning and production scheduling. In this paper, the processing routes and processing machine are selected through chromosome crossover and mutation, in order to implement the optimal scheduling of the flexible workshop production. Meanwhile, a performance test about the integration of process planning and production scheduling is implemented, and the results shows that the genetic algorithm is efficient to obtain optimal or near optimal process routes which can meet the requirements of production scheduling.     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling was implemented after process plans had been generated. Considering their complementarity, it is necessary to integrate these two functions more tightly to improve the performance of a manufacturing system greatly. In this paper, a mathematical model of integrated process planning and scheduling has been formulated. And, an evolutionary algorithm-based approach has been developed to facilitate the integration and optimization of these two functions. To improve the optimized performance of the approach, efficient genetic representation and operator schemes have been developed. To verify the feasibility and performance of the proposed approach, experimental studies have been conducted and comparisons have been made between this approach and some previous works. The experimental results show that the integrated process planning and scheduling is necessary and the proposed approach has achieved significant improvement.     

Using multi-population intelligent genetic algorithm to find the pareto-optimal parameters for a nano-particle milling process

Nano-particle materials have been widely applied in many industries and the wet-type mechanical milling process is a popular powder technology to produce the nano-particles. Since the milling process involves a number of process parameters and the multi-objective quality criteria, it is very important to set the optimal milling process parameters in order to achieve the desired multiple quality criteria. In this study, a new multi-objective evolutionary algorithm (MOEA), called the multi-population intelligent genetic algorithm (MPIGA), is proposed to find the optimal process parameters for the nano-particle milling process. In the new method, the orthogonal array (OA) experiment is first applied to obtain the analytic data of the milling process. Then the response surface method (RSM) is applied to model the nano-particle milling process and to determine the objective (fitness) value. The generalized Pareto-based scale-independent fitness function (GPSIFF) is then used to evaluate the Pareto solutions. Finally, the MPIGA is proposed to find the Pareto-optimal solutions. The results show that the integrated MPIGA approach can generate the Pareto-optimal solutions for the decision maker to determine the optimal parameters and to achieve the desired product qualities for a nano-particle milling process.     

Toolpath planning algorithm for the ablation process using energy sources

Ablation processes using various energy sources such as lasers, electrical power and ultrasonic sources have been widely used in industry to ablate workpieces made of hard-to-cut and temperature resistive materials. Even though the cutting mechanism of the ablation process is different from that of the mechanical cutting process, an identical toolpath of the mechanical cutting process has been applied to the ablation process. An inappropriate toolpath for the ablation process may result in a lower dimensional accuracy of the ablated part. Therefore, a new toolpath planning algorithm considering the characteristics of the energy source is required. In this paper, a toolpath planning algorithm for the ablation process is proposed. The proposed algorithm consists of three steps: (1) The generation of a valid toolpath element, (2) the storage of toolpath elements and the creation of sub-groups, (3) the linking of sub-groups. New guidelines on the toolpath demanded by the ablation process are studied. A new idea involving the use of a storage matrix is applied to the storage of toolpath elements and the creation of sub-groups. In order to verify the applicability of the proposed algorithm, the proposed toolpath planning algorithm has been implemented and tested with practical examples.     

An integrated multi-objective immune algorithm for optimizing the wire bonding process of integrated circuits

Optimization of the wire bonding process of an integrated circuit (IC) is a multi-objective optimization problem (MOOP). In this research, an integrated multi-objective immune algorithm (MOIA) that combines an artificial immune algorithm (IA) with an artificial neural network (ANN) and a generalized Pareto-based scale-independent fitness function (GPSIFF) is developed to find the optimal process parameters for the first bond of an IC wire bonding. The back-propagation ANN is used to establish the nonlinear multivariate relationships between the wire boning parameters and the multi-responses, and is applied to generate the multiple response values for each antibody generated by the IA. The GPSIFF is then used to evaluate the affinity for each antibody and to find the non-dominated solutions. The “Error Ratio” is then applied to measure the convergence of the integrated approach. The “Spread Metric” is used to measure the diversity of the proposed approach. Implementation results show that the integrated MOIA approach does generate the Pareto-optimal solutions for the decision maker, and the Pareto-optimal solutions have good convergence and diversity performance.     

Robust nonlinear channel equalization using WNN trained by symbiotic organism search algorithm

In the present world of ‘Big Data,’ the communication channels are always remaining busy and overloaded to transfer quintillion bytes of information. To design an effective equalizer to prevent the inter-symbol interference in such scenario is a challenging task. In this paper, we develop equalizers based on a nonlinear neural structure (wavelet neural network (WNN)) and train it's weighted by a recently developed meta-heuristic (symbiotic organisms search algorithm). The performance of the proposed equalizer is compared with WNN trained by cat swarm optimization (CSO) and clonal selection algorithm (CLONAL), particle swarm optimization (PSO) and least mean square algorithm (LMS). The performance is also compared with other equalizers with structure based on functional link artificial neural network (trigonometric FLANN), radial basis function network (RBF) and finite impulse response filter (FIR). The superior performance is demonstrated on equalization of two non-linear three taps channels and a linear twenty-three taps telephonic channel. It is observed that the performance of the gradient algorithm based equalizers fails in the presence of burst error. The robustness in the performance of the proposed equalizers to handle the burst error conditions is also demonstrated.     

Multi-objective evolutionary design and knowledge discovery of logic circuits based on an adaptive genetic algorithm

Evolutionary design of circuits (EDC), an important branch of evolvable hardware which emphasizes circuit design, is a promising way to realize automated design of electronic circuits. In order to improve evolutionary design of logic circuits in efficiency, scalability and capability of optimization, a genetic algorithm based novel approach was developed. It employs a gate-level encoding scheme that allows flexible changes of functions and interconnections of logic cells comprised, and it adopts a multi-objective evaluation mechanism of fitness with weight-vector adaptation and circuit simulation. Besides, it features an adaptation strategy that enables crossover probability and mutation probability to vary with individuals' diversity and genetic-search process. It was validated by the experiments on arithmetic circuits especially digital multipliers, from which a few functionally correct circuits with novel structures, less gate count and higher operating speed were obtained. Some of the evolved circuits are the most efficient or largest ones (in terms of gate count or problem scale) as far as we know. Moreover, some novel and general principles have been discerned from the EDC results, which are easy to verify but difficult to dig out by human experts with existing knowledge. These results argue that the approach is promising and worthy of further research.     

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.     

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.