A NEW HEURISTIC-EM FOR PERMUTATION FLOWSHOP SCHEDULING

There is a growing consensus that 3D printing technologies will be one of the next major technological revolutions. Over the past few years, many studies and researches have been carried out to improve and enhance the functions and performance of the 3D printers. In this paper, a modified Electromagnetism-like Mechanism (EM) algorithm is proposed to search for the optimum printing path of a 3D printer. The machine is set to fabricate the bracket of a photovoltaic solar energy harvesting panel. Instead of randomly setting the search step size, the improved EM systematically fine-tunes the steps to search for the best printing path. The performance of the enhanced EM is benchmarked with the conventional EM and Genetic Algorithm (GA). The results presented in this paper shows that the modified EM outperformed all other optimization techniques in terms of time taken, distance traveled and overall convergence process. We can thus conclude that the proposed modified EM performs well in optimizing the path planning sequence of a 3D printer.

electromagnetism-like mechanism (em) algorithm, a meta-heuristic algorithm for global optimisation, utilises an attraction-repulsion mechanism to move the sample points towards the optimality. the original em has a strong ability for diversification. and a simple random line search algorithm for local search has been added to improve its intensification. however, it plays a quite limited role and the performance of the original em is not satisfactory. therefore, in this paper, a modified solis and wets local search is proposed to improve the performance of em. two self-adapt parameters are introduced into solis and wets local search. in addition, an accelerated force formula is adopted for high-dimensional function optimisation. some benchmark test problems have been used to evaluate the proposed-algorithm. results obtained are compared with those from other algorithms including original em algorithm and all kinds of particle swarm optimisation methods. the comparisons show that the novel em has achieved significant improvement.

In this paper, a meta-heuristic algorithm (electromagnetism-like mechanism, EM) for fuzzy neural network training is introduced. Electromagnetism-like mechanism simulates the electromagnetism theory of physics by considering each sample point to be an electrical charge. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. Besides, the electromagnetism-like mechanism is not easily falling into local optimum. Therefore, the purpose of this study is to use the electromagnetism-like mechanism to develop the fuzzy neural networks (EMFNN), and employ this EMFNN to train fuzzy if-then rules. According to the case, the EMFNN could successfully generalize new fuzzy if-then rules.KeywordsLocal SearchFuzzy NumberFuzzy Neural NetworkFuzzy WeightSales ForecastThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An improved electromagnetism-like mechanism algorithm for constrained optimization

This paper presents an electromagnetism-like mechanism (EM) algorithm in order to solve cell formation problems. Based on the attraction-repulsion principle in electromagnetic theory, each combination of the part and machine groups was regarded as a charged particle. The total force calculation and the move along the total force calculation were then determined out to either maximize the cell group effectiveness or minimize the exceptional elements (EE) and the number of vacancies (NV). A comparison with other algorithms in the related literature found that the final result of the EM algorithm met the expected quality in terms of the exceptional elements and number of vacancies, regardless of whether or not new cells were added. Key words: Cellular manufacturing system, group technology, electromagnetic-like mechanism.

Due to the complex nature of training neural network (NN), this problem has gained popularity in the nonlinear optimization field. In order to avoid falling into local minimum because of inappropriate initial weights, a number of global search techniques are developed. This paper applies a novel global algorithm, which is electromagnetism-like mechanism (EM) algorithm, to train NN and the EM based algorithm for neural network training is presented. The performance of the proposed algorithm is evaluated in classification problems and the comparison with BP and GA algorithms shows its effectiveness.

Simultaneous adjustment of all cutter locations in a tool path using meta-heuristic algorithms provides a systematic approach to reducing machining errors in 5-axis flank machining of ruled surfaces. However, these algorithms experienced unsatisfactory quality of optimal solutions and lengthy search time in high-dimensional search space. To solve these problems, we propose an iterative optimization scheme that progressively simplifies solution space by sampling techniques. Akaike information criterion (AIC) is used to screen significant factors from sampling data. An electromagnetism-like mechanism (EM) algorithm searches through a simplified solution space constructed only using these factors. An iteration process consisting of such sampling, screening, and searching steps repeats several times until final optimal solutions are obtained. Test results of representative surfaces validate the effectiveness of the proposed scheme. Both solution quality and search efficiency are improved comparing to those produced by previous studies. This work enhances the practicality of optimization-driven tool path planning in 5-axis flank machining.

Nonlinear systems design by a novel fuzzy neural system via hybridization of electromagnetism-like mechanism and particle swarm optimisation algorithms

A hybridization of simulated annealing and electromagnetism-like mechanism for a periodic job shop scheduling problem

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

Many applications involve the concepts of scheduling, such as communications, packet routing, production planning [Zhai et al., 2006], classroom arrangement [Mathaisel & Comm, 1991], aircrew scheduling [Chang, 2002], nurse scheduling [Ohki et al., 2006], food industrial [Simeonov & Simeonovova, 2002], control system [Fleming & Fonseca, 1993], resource-constrained scheduling problem [Chen, 2007] and grid computing. There are many different types of scheduling problems such as real-time, job-shop, permutation flow-shop, project scheduling and other scheduling problems have been studied intensively. However, in this work, the studied grid task scheduling problem is much more complex than above stated classic task scheduling problems. Restated, a grid application is regarded as a task scheduling problem involving tasks with inter-communication and distributed homogeneous or heterogeneous resources, and can be represented by a task interaction graph (TIG). Grid is a service for sharing computing power and data storage capacity over the Internet. The grid systems outperform simple communication between computers and aims ultimately to turn the global network of computers into one vast computational resource. Grid computing can be adopted in many applications, such as high-performance applications, large-output applications, data-intensive applications and community-centric applications. These applications major concern to efficiently schedule tasks over the available multi-processor environment provided by the grid. A grid is a collaborative environment in which one or more tasks can be submitted without knowing where the resources are or even who owns the resources [Foster et al., 2001]. The efficiency and effectiveness of grid resource management greatly depend on the scheduling algorithm [Lee et al., 2007]. Generally, in the grid environment, these resources are different over time, and such changes will affect the performance of the tasks running on the grid. In grid computing, tasks are assigned among grid system [Salman, 2002]. The purpose of task scheduling in grid is to find optimal task-processor assignment and hence minimize application completion time (total cost). Most scheduling problems in these applications are categorized into the class of NP-complete problems. This implies that it would take amount of computation time to obtain an optimal solution, especially for a large-scale scheduling problem. A variety of approaches have been applied to solve scheduling problems, such as

Solving an integrated scheduling and routing problem with inventory, routing and penalty costs

An Iterative local-search framework for solving constraint satisfaction problem

Local search is an established meta heuristic for scheduling problems. To avoid of getting stucked in a local optimum, the achieved solution is destroyed. Usually, this is done randomly. Here, two problem specific procedures are suggested. One focuses on jobs with the highest difference between the actual processing time and the net processing time of the job and the other one tries to remove jobs which causes the highest idle time for themselves or other jobs. Comprehensive simulations of test scheduling problems as well as a real world application show that both problem specific procedures outperform a random procedure.

An effective multi-objective evolutionary algorithm for solving the AGV scheduling problem with pickup and delivery