Fixed charge solid transportation problem (FCSTP) is one of the main and most important problems in transportation and network res(EA)rch ar(EA)s. To tackle such an NP-hard problem, An ELECTROMAGNETISM-like ALGORITHM (EM) is employed. To the best of our knowledge, EM has been considered for any kind of transportation problems. Due to the significant role of parameters on the ALGORITHM’s performance, a calibration in EM is carried out with the aid of a set of experimental design. The efficiency of employed parameters is m(EA)sured by the experimental design method. To evaluate the performance of the proposed EM, a computational study has been conducted and the associated results obtained by the EM are compared with simulated ann(EA)ling ALGORITHM (SA).

An ELECTROMAGNETISM ALGORITHM is a meta-heuristic proposed to derive approximate solutions for computationally hard problems. In the literature, several successful applications have been reported for graph-based optimization problems, such as scheduling problems. This paper presents an application of the ELECTROMAGNETISM ALGORITHM to supplier selection in a production planning process where there are multiple products and multiple customers and also capacity constraints. We consider a situation where the demand quantity of multiple discrete products is known over a planning horizon. The required raw material for (EA)ch of these products can be purchased from a set of approved suppliers. Also, a demand-dependent delivery time (due date) and maximum delivery time (d(EA)dline) apply for (EA)ch demand. Problems containing all these assumptions have not been addressed previously in the literature. A decision needs to be made regarding what raw material to order and in what quantities, which suppliers and, finally, at which periods. Numerical results indicate that the ELECTROMAGNETISM ALGORITHM exhibits impressive performances with small error ratios. The results support the success of the ELECTROMAGNETISM ALGORITHM application to the supplier selection problem of interest.

In this paper, two different sub-problems are considered to solve a resource constrained project scheduling problem (RCPSP), namely i) assignment of modes to tasks and ii) scheduling of these tasks in order to minimize the make span of the project. The modified ELECTROMAGNETISM-like ALGORITHM d(EA)ls with the first problem to cr(EA)te an assignment of modes to activities. This list is used to generate a project schedule. When a new assignment is made, it is necessary to fix all mode dependent requirements of the project activities and to generate a random schedule with the serial SGS method. A local s(EA)rch will optimize the sequence of the activities. Also in this paper, a new penalty function has been proposed for solutions which are inf(EA)sible with respect to non-renewable resources. Performance of the proposed ALGORITHM has been compared with the best ALGORITHMs published so far on the basis of CPU time and number of generated schedules stopping criteria. Reported results indicate excellent performance of the ALGORITHM.

This paper studies the multi-stage supply chain system (MSSCM) controlled by the kanban mechanism. In the kanban system, decision making is based on the number of kanbans as well as batch sizes. A kanban mechanism is employed to assist in linking different production processes in a supply chain system in order to implement the scope of just-in-time (JIT) philosophy. For a MSSCM, a mixed-integer nonlin(EA)r programming (MINLP) problem is formulated from the perspective of JIT delivery policy where a kanban may reflect to a transporter. Since the adopted model is of MINLP type and solving it by branch and bound (B& B) takes time, a metaheuristic is presented. This metaheuristic is an electromagnetic ALGORITHM ((EA)). The (EA) is compared against an existing ALGORITHM and also B& B results to evaluate the proposed metaheuristic. Extensive experiments and statistical analyses demonstrate that our proposed EM is more efficient than B& B with regard to the objective functions considered in this paper.

In this paper, we study a flow shop batch processing machines scheduling problem. The fuzzy due dates are considered to make the problem more close to the r(EA)lity. The objective function is taken as the weighted sum of fuzzy (EA)rliness and fuzzy tardiness. In order to tackle the given problem, we propose a hybrid ELECTROMAGNETISM-like (EM) ALGORITHM, in which the EM is hybridized with a diversification mechanism and effective local s(EA)rch to enhance the efficiency of the ALGORITHM.The proposed ALGORITHMs are evaluated by comparison against two existing well-known EMs in the literature. Additionally, we propose some heuristics based on the (EA)rliest due date (EDD) to solve the given problem. The proposed hybrid EM ALGORITHM is tested on sets of various randomly generated instances. For this purpose, we investigate the impacts of the rise in problem sizes on the performance of the developed ALGORITHM. Through the analysis of the experimental results, the highly effective performance of the proposed ALGORITHM is shown against the two existing well-known EMs from the literature and proposed EDDs.

The aim of the present study investigated the psychometric properties of Esoteric Analogies test in college students. The sample of this study was 250 female graduate students who were selected of Shahid Chamran University of Ahvaz by multi-stage cluster sampling method. The instruments of this study consisted of Esoteric Analogies ((EA)) test, Memory & R(EA)soning Competence Inventory (MARCI) and General Self-efficacy Scale (GSE). The results of the present study indicate Esoteric Analogies test had good internal consistency. Also, results rev(EA)led that there are a significant positive correlation between Esoteric Analogies test with Memory & R(EA)soning Competence Inventory, R(EA)soning Competence subscale and General Self-efficacy Scale. In addition, confirmatory factor analysis showed Esoteric Analogies test fitted the data at an acceptable level (AGFI=0/81) and could be used in females sample.

There are several approaches to the concept of "Filed" in the classical theory of ELECTROMAGNETISM; Operationalism and R(EA)lism are two important strategies in this regard. I this paper the R(EA)listic view of Marc Lang is introduced as well as the empiricist views of Carnap and Bridgman. Lang in his famous book "An Introduction to the Philosophy of Physics" suggests two essential questions about "action at distance" and "r(EA)lity of fields" and after a proper discussion conclude that the Classical Physics is not capable of dissolving the problems relating the locality and the ontology of energy and field, and it is the mission of the Special Theory of Relativity.

Orthogonal Frequency Division Multiplexing (OFDM) is a useful technology in wireless communications that provides high-rate data transmission in multipath fading channels. The advantages of OFDM systems are the high spectral efficiency and strong resistance to frequency selective fading. In OFDM systems, a large number of sub-carriers are used to modulate the symbols causing the time-domain OFDM signal to have a large dynamic range, or a high p(EA)k-to-average power ratio (PAPR). When the signals are applied to a nonlin(EA)r power amplifier, the OFDM systems’ performance is degraded by the high PAPR. In recent y(EA)rs, several works have been done to reduce the PAPR of OFDM systems. One of the most well-known methods is a partial transmit sequence (PTS). Regardless of the PTS advantages, it suffers from a high computational complexity. Because it requires an exhaustive s(EA)rch over all possible combinations of phase factors. The computational complexity of the PTS incr(EA)ses with incr(EA)sing the number of phase factors and sub-blocks. There are several approaches to overcome the computation complexity issue of the PTS technique. The majority of these methods mainly employed swarm intelligence and evolutionary optimization ALGORITHMs to resolve the PTS shortcoming. These methods report encouraging results, however, their performance is far from the id(EA)l state. This highlights that improving the performance of PTS is an open problem and there is room for more improvement. As an element of res(EA)rch, we propose an optimization approach based on the election ALGORITHM ((EA)) to overcome the computational complexity of the PTS technique. To r(EA)lize this goal, we improve the (EA) ALGORITHM by introducing a new version of positive advertisements operator. The new operator efficiently improves the s(EA)rch capability of the (EA) through balancing between the exploration and exploitation power of the ALGORITHM. The proposed (EA) based PTS ((EA)-PTS) approach, by s(EA)rching the optimal phase factors, imposes less computational complexity on the system and reduces the PAPR to an acceptable level. The proposed method is compared with the optimal PTS (O-PTS), genetic ALGORITHM-based PTS (GA-PTS) and imperialism competition ALGORITHM based PTS (ICA-PTS) techniques. Simulation results show that the proposed (EA)-PTS has better performance in simultaneously reducing the PAPR and computational complexity.