Electromagnetic-like Mechanism Research Articles

Multipath Routing Algorithm Using an Electromagnetic-Like Mechanism with Threshold Acceptance for Mobile Ad Hoc Networks

A number of routing algorithms have been developed to facilitate routing in mobile ad hoc network (MANET). However, most algorithms merely implement the shortest path routing and do not adhere well to MANET characteristics, such as the trust and battery levels of the mobile nodes, crucially influence the suitability of the routing decision. In this paper, a new routing algorithm called electromagnetic-like mechanism routing (EMR) algorithm is proposed. The EMR is based on the electromagnetic-like mechanism (EM), which is an artificial intelligent algorithm with the potential for continuous types of problem optimization by simulating the attraction---repulsion mechanism of the electromagnetism theory. However, EM has to incorporate different types of search engines for it to be incorporated into MANET routing. Therefore, the updated quality threshold accepting algorithm is developed as the new decision making system of the search engine to be incorporated with the EM for route optimization and to build the EMR algorithm. The EMR algorithm selects the best route in MANET by optimizing the hop count, trust level, and battery-power level of the mobile nodes. The simulation results show that the EMR algorithm achieves better results in terms of packet delivery, normalized routing load, end-to-end delay, and throughput.

Scheduling of Multimodal Activities with Multiple Renewable and Availability Constrained Resources under Stochastic Conditions

Abstract We address the stochastic multi-mode resource-constrained project scheduling problem in the case where the project activities require multiple renewable resources, constrained by total availability. In our study, we introduce uncertainty by characterizing, in each activity, each required resource with a work content following some known distribution. Furthermore, the set of feasible resource allocations is a continuous and well defined interval of real values. In turn, any allocation must also comply with the resource maximum total availability at any point during the project execution. We propose a mathematical model that relies on a discrete and finite partition of the timespan when evaluating activity schedules and enforces equal durations yielded by each required resource, within each activity, in expectation. The objective is to determine the start times and the mode (defined by the resource allocations) which minimize the expected total project cost. We implemented a method relying upon two global optimization metaheuristics - the electromagnetic-like mechanism and the evolutionary algorithm to solve the problem.

Feature subset selection using improved binary gravitational search algorithm

Feature selection is one of the important activities in various fields such as computer vision and pattern recognition. In this paper, an improved version of the binary gravitational search algorithm BGSA is proposed and used as a tool to select the best subset of features with the goal of improving classification accuracy. By enhancing the transfer function, we give BGSA the ability to overcome the stagnation situation. This allows the search algorithm to explore a larger group of possibilities and avoid stagnation. To evaluate the proposed improved BGSA IBGSA, classification of some well known datasets and improving the accuracy of CBIR systems are experienced. Results are compared with those of original BGSA, genetic algorithm GA, binary particle swarm optimization BPSO, and electromagnetic-like mechanism. Comparative results confirm the effectiveness of the proposed IBGSA in feature selection.

An electromagnetism-like mechanism for solving cell formation problems

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.

An electromagnetism-like mechanism for solving cell formation problems

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.

Group scheduling in flexible flow shops: a hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism

This paper applied a novel evolutionary algorithm, imperialist competitive algorithm (ICA), for a group scheduling problem in a hybrid flexible flow shop with sequence-dependent setup times by minimising maximum completion time. This algorithm simulates a social-economical procedure, imperialistic competition. Initial population is generated randomly and evolution is carried out during the algorithm. Firstly individuals, countries, are divided into two categories: imperialists and colonies. Imperialist competition will occur among these empires. This competition will increase some empires authority by ruining a weak empire and dividing its colonies among others. Electromagnetic-like mechanism concepts are employed here to model the influence of the imperialist on their colonies. The algorithm will continue until one imperialist exists and possesses all countries. In order to prevent carrying out extensive experiments to find optimum parameters of the algorithm, we apply the Taguchi approach. The computational results are compared with the outstanding benchmark on the flow shop scheduling problem, random key genetic algorithms (RKGA), and it shows superiority of the ICA.

An integrated hybrid approach to the examination timetabling problem

This paper is derived from an interest in the development of automated approaches to tackle examination timetabling problems effectively. We propose a hybrid approach that incorporates effective heuristic operators within the great deluge algorithm. The latter was chosen because of consistently good performances being reported within the examination timetabling research domain. The additional heuristic procedures further enhance the overall effectiveness of this integrated hybrid approach. These procedures are drawn from methodologies that have appeared in the literature under term the “electromagnetic-like mechanism”. The aim is to move sample points towards a high quality solution while avoiding local optima by utilising a calculated force value. This value, which is calculated dynamically, is treated as a decay rate in determining the level within the great deluge algorithm. To evaluate the proposed algorithm, we carry out experimental work on two types of examination timetabling datasets. All the related results and analysis obtained illustrate that this hybrid approach is effective when compared with existing approaches in the literature.

A hybrid metaheuristic approach to the university course timetabling problem

This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or `level'. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the `level' within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.

A hybrid algorithm based on particle swarm optimization and simulated annealing for a periodic job shop scheduling problem

Generating schedules such that all operations are repeated every constant period of time is as important as generating schedules with minimum delays in all cases where a known discipline is desired or obligated by stakeholders. In this paper, a periodic job shop scheduling problem (PJSSP) based on the periodic event scheduling problem (PESP) is presented, which deviates from the cyclic scheduling. The PESP schedules a number of recurring events as such that each pair of event fulfills certain constraints during a given fixed time period. To solve such a hard PJSS problem, we propose a hybrid algorithm, namely PSO-SA, based on particle swarm optimization (PSO) and simulated annealing (SA) algorithms. To evaluate this proposed PSO-SA, we carry out some randomly constructed instances by which the related results are compared with the proposed SA and PSO algorithms as well as a branch-and-bound algorithm. In addition, we compare the results with a hybrid algorithm embedded with electromagnetic-like mechanism and SA. Moreover, three lower bounds (LBs) are studied, and the gap between the found LBs and the best found solutions are reported. The outcomes prove that the proposed hybrid algorithm is an efficient and effective tool to solve the PJSSP.

A hybridization of simulated annealing and electromagnetic-like mechanism for job shop problems with machine availability and sequence-dependent setup times to minimize total weighted tardiness

In this paper, we explore job shop problems with two recently popular and realistic assumptions, sequence-dependent setup times and machine availability constraints to actualize the problem. The criterion is a minimization of total weighted tardiness. We establish a simple criterion to integrate machine availability constraints and scheduling decisions simultaneously. We propose a hybrid meta-heuristic to tackle the given problem. This meta-heuristic method, called EMSA, is a combination of two meta-heuristics: (1) Electromagnetic-like mechanism (EM); and (2) simulated annealing (SA). The hybridization is done to overcome some existing drawbacks of each of these two algorithms. To evaluate the proposed hybrid meta-heuristic method, we carry out a benchmark by which the proposed EMSA is compared with some existing algorithms as well as simulated annealing and electromagnetic-like mechanism alone in a fixed given computational time. All the related results and analysis obtained through the benchmark illustrate that our proposed EMSA is very effective and supersedes the foregoing algorithms.