Electromagnetism-like Algorithm Research Articles

Optimization of the routing problem using the physics-based electromagnetism-like algorithm

Waste collection is considered as a first objective in planning a sustainable waste management, and provision of substantial collection coverage in urban and rural areas should be satisfied before investing in more sophisticated infrastructure. Therefore, good waste collection service is essential to upgrade further the next levels of a municipal solid waste system. One of the ways to enhance performance on waste collection is to plan effective routing of trucks. The study formulated a Waste Collection Arc Routing Problem to model the waste collection routing problem of Parang, Marikina City. The study revealed that the EM-like with local search and selection strategy mechanism displayed significant minimization abilities of distance values, and the capacity consideration of truck is important to attain better efficiency and cost savings of route.

A hybrid combined algorithm based on intelligent water drops and electromagnetism-like algorithms for fuzzy TSP1

This paper presents a new combined algorithm for the fuzzy Travelling Salesman Problem (FTSP) based on a composition of the Intelligent Water Drops (IWD) and the Electromagnetism-like (EM) algorithms. In a FTSP, the time consumed distance between cities i and j can be described by vague knowledge, such as fuzzy quantity. The main goal of FTSP is to achieve the minimum distance of Hamilton circuit of G graph, where the Hamilton circuit is a closed route of cities (i.e., nodes) of G that have been visited only once. The proposed algorithm transfers the generated responses by the IWD to the EM, where the best answer is selected. Importantly, the computed results from both algotithm are compared and the best is accumulated. In other words, in each iteration, the best result is collected by comparison between the current and previous hierarchies until the halt condition is fulfilled. Finally, the results of the genetic algorithm (GA), IWD and EM algorithms are compared, so that the efficiency of the proposed combined IWD-EM algorithm is determined.

Coverage analysis and a new metaheuristic approach using the Elfes Probabilistic detection model in Wireless sensor networks

The dynamic deployment of sensors in AoI (Area of Interest) plays an important role in ensuring the quality of services (QoS) of Wireless Sensor Networks (WSNs) by optimising the coverage and lifetime of the network. In this paper, a new dynamic deployment approach using metaheuristics based on Electromagnetism-Like (EM-L) algorithm and Elfes Probabilistic Detection Model (EPDM) was proposed to optimise the coverage of WSNs based on the sensor coverage problem, and coverage analysis of AoI was performed. A variable threshold detection probability (TDP) was defined instead of defining a fixed TDP as in the literature. Thus, a more realistic modelling environment was created by considering the signal-to-noise ratio (SNR) in the coverage calculation. The simulation results show that the sensors are always effectively deployed in different scenarios with variable TDPs by the proposed approach compared to a random distribution.

EXTRACTION OF DOUBLE-DIODE PHOTOVOLTAIC MODULE MODEL’S PARAMETERS USING HYBRID OPTIMIZATION ALGORITHM

This paper presents seven parameters of double diode model of the photovoltaic module under different weather conditions are extracted using differential development with an integrated mutation per iteration (DEIM) algorithm. The algorithm is produced by integrating of two other algorithms namely, electromagnetism like (EML) and differential evolution (DE) algorithms. DEIM enhances the mutation step of the original DE by using the attraction-repulsion principle found in the EML algorithm. Meanwhile, a novel strategy based on adjusting mutation and crossover rate factors for each iteration is adopted in this paper. The implemented scheme's success is confirmed by comparing its results with those obtained by techniques cited in the literature. Along with the results, the DEIM suggests high closeness with the experimental I–V characteristic. For the proposed algorithm an average Root Mean Square Error (RMSE), absolute error (AE), mean bias Error (MBE), and execution time values were 0.0608, 0.044, 0.0053 and 23.333, respectively. The comparisons and evaluations results proved that the DEIM is better in terms of precision and rapid convergence. Furthermore, fewer control parameters are needed in comparison to EML and DE algorithms.

A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms

A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO2)emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran.

Electromagnetism-Like Algorithm-Based Parameters Estimation of Double-Diode PV-Module Model

An accurate parameters extraction of photovoltaic (PV) module is necessary as it plays an essential role in determining the performance of PV system. Therefore, an electromagnetism-like algorithm (EMLA) is proposed to optimally estimate associated parameters of double-diode PV module model. The parameters are optimized based on a proposed fitness function, which depends on the root mean square error between the experimental and computed PV output current. Seven different experimental data sets of I-V curve are utilized to verify the presented approach under various operation conditions. The results show there is a high fitness between the estimated and realistic I-V curves under different weather conditions. Furthermore, the results are validated with another model that proposed in literature under different statistical indices. Finally, the proposed PV modeling method exhibits under all operation conditions an average root mean square error, average mean bias error and average determination coefficient under all operation conditions were 0.0799, 0.0088, and 0.9863, respectively.

Bi-Objective Optimization of Service-Oriented Location-Pricing Model Using Electromagnetism-Like Mechanism Algorithm

This paper develops a multi-objective multi-layer location-pricing (MLLP) model with congested facilities in which the facilities act like a classic queuing system. The customers who arrive to this system receive service at all layers in a predetermined order to fulfill their demands. The goal is to determine (1) optimal number of the facilities required at each layer, (2) optimal allocation of customers to facilities, and (3) optimal price of providing service at each layer. The objective functions are to maximize the total profit of the system and to minimize the sum of travel and waiting times, simultaneously. The problem is formulated as a multi-objective nonlinear integer mathematical programming model. Since the problem is hard to be solved analytically, we present a multi-objective meta-heuristic algorithm (MHA) based on an electromagnetism-like mechanism (ELM) as a solution for multi-objective MLLP. This algorithm used an elitist mechanism to strengthen the structure of search engine in order to find better quality solutions. The results indicate the efficiency and effectiveness of the proposed algorithm in comparison with the traditional ELM.

A New Proposed Pendulum-Like with Attraction-Repulsion mechanism Algorithm for Solving Optimization Problems

This work aims to contribute to the domain of new modification in electromagnetism-like (EM) algorithm, which will be referred to in this work as pendulum-like with attraction-repulsion mechanism algorithm (PA). PA algorithm has been constructed with the purpose of solving inverse kinematics (IK) problem. The proposed new algorithm mimics the concepts of a simple physical pendulum and is derived as a local search in order to promote the performance of the EM algorithm. Comparisons between the suggested PA with the available algorithms, such as genetic algorithm (GA) and EM were conducted using two mathematical test functions. On top of the testing, the PA algorithm is tested to solve the problem of IK for a four degree-of-freedom (DOF) planar robot. The simulation results indicate that the PA algorithm outperforms other approaches in terms of accuracy and the speed of convergence through the errors and the decreasing of objective with iterations as mention in presented tables and figures, on top of being able to successfully solve the IK problem with multiple robot configurations.

Improving additive manufacturing performance by build orientation optimization

Additive manufacturing (AM) is an emerging type of production technology to create three-dimensional objects layer-by-layer directly from a 3D CAD model. AM is being extensively used in several areas by engineers and designers. Build orientation is a critical issue in AM since it is associated with the part accuracy, the number of supports required and the processing time to produce the object. This paper presents an optimization approach to solve the part build orientation problem taking into account some characteristics or measures that can affect the accuracy of the part, namely the volumetric error, the support area, the staircase effect, the build time, the surface roughness and the surface quality. A global optimization method, the Electromagnetism-like algorithm, is used to solve the part build orientation problem.

Estimation of photovoltaic module model’s parameters using an improved electromagnetic-like algorithm

This paper offers an electromagnetism-like (IEM) algorithm to estimate the five parameters of a single-diode PV module’s model. IEM uses local search and improves movement step to increase the convergence to optimal solutions. The key of improvement is performed by adding a nonlinear equation to adjust the length of the particle in each iteration. Moreover, the total force formula is simplified to speed up the exploration for an optimal solution. Analyses are carried out by experimental data points at various operational conditions to show the stability and reliability of the proposed methods. The results of the proposed IEM algorithm show a better convergence speed and high accuracy compared with other models in the literature, which involves various statistical errors. The values of average root mean square error, mean bias error, standard deviation, average absolute error, and average test statistic of the proposed method are 589%, 0.51%, 0.19%, 46%, and 0.53, respectively. As a conclusion, the IEM algorithm presents better performance than other methods in the literature in terms of accuracy and convergence.

PTS scheme based on MCAKM for peak‐to‐average power ratio reduction in OFDM systems

This study considers the application of the partial transmit sequence (PTS) technique to reduce the peak-to-average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. The conventional PTS approach has proved its high effectiveness in PAPR reduction for OFDM signals where the reduction performance of PAPR depends mainly on phase rotation factors. Finding the optimal phase rotation factors requires an exhaustive computationally task, which is a serious implementation problem in practical systems. To fix this issue of computational complexity and reduce the PAPR at the same time, in the present work, a novel PTS approach based on multi-population cultural algorithm adopting knowledge migration (MCAKM) has been proposed for optimal phase rotation factors search. Computer simulation results show that the proposed MCAKM–PTS technique leads to a better PAPR reduction and bit error rate performance with a significant reduction in computational complexity compared to the conventional PTS and the previously optimisation algorithms applied to PTS such as particle swarm optimisation, artificial bee colony, and electromagnetism-like algorithm.

A Novel Modified Electromagnetism-like Algorithm for Solving Constrained Optimization Problem

The original Electromagnetism-like mechanism (EM) is a meta-heuristic algorithm utilizing an attraction-repulsion mechanism (called as force F) to move sample points towards optimality in global optimization problems. To solve the potential problem of stagnation happening in the original algorithm, new searching procedures were proposed in modified algorithm to determine the next search direction and step length of points in the paper. Compared to the original algorithm, tri-factors of the best historical visited positions of each point, best point and total force F were considered in the improved searching procedures. Moreover, the feasibility and dominance (FAD) rules were incorporated to extend the proposed algorithm to solve constrained optimization problem. Finally, Preliminary experiments verified the effectiveness of the proposed algorithm.

Photovoltaic Parameters Identification Using Optimization Algorithm

Solar energy is regarded as an essential source of electricity generation. It is one of the renewable energy sources that have the potential for future energy applications. The optimization algorithms, such as the Electromagnetism-like (EM) algorithm is suitable for tackling numerous optimization problems. The EM algorithm has the ability to handle nonlinear functions, and it is imagined to be extremely productive for the parameters estimation of a photovoltaic (PV) module. Different values of control parameters of the method have been tested in this paper. This is done to demonstrate the impact of every parameter on the performance of the algorithm regarding the speed of convergence and precision. In every case of parameters, the algorithm can extract the photovoltaic model parameters. The objective function for every arrangement of values has been resolved and then contrasted with others in order to select the best case.

A constrained portfolio selection model at considering risk-adjusted measure by using hybrid meta-heuristic algorithms

Portfolio selection is a key issue in the business world and financial fields. This article presents a new decision making method of portfolio optimization (PO) issues in different risk measures by using new evolutionary computing method and cardinality constrains which is mentioned as hybrid meta-heuristic algorithms. Based on mean–variance (MV) Method by Markowitz we collected three risk levels; mean absolute deviation (MAD), semi variance (SV) and variance with skewness (VWS). The developed algorithms are Electromagnetism-like algorithm (EM), particle swarm optimization (PSO), genetic algorithm (GA), genetic network programming (GNP) and simulated annealing (SA). Also a diversification mechanism strategy is implemented and hybridized with the developed algorithms to increase the diversity and overcome local optimality. The sustainability of this proposed model is verified by 50 factories on the Iranian stock exchange. Finally, experimental results of proposed algorithms with cardinality constraint are compared with each other by four effective metrics in which the algorithms performance for achieving the optimal solution discussed. In addition, we have done the analysis of variance technique to confirm the validity and accurately analyze of the results which the success of this method was proved.

A novel hybrid method combining electromagnetism-like mechanism and firefly algorithms for constrained design optimization of discrete truss structures

A new optimization method called Electromagnetism-like Firefly Algorithm (EFA), which is a novel hybrid between the Electromagnetism-like Algorithm (EM) and the Firefly Algorithm (FA) for discrete structural optimization is proposed. The EFA inherits the advantages of both the FA and the EM. This proposed optimization algorithm is then presented to improve both solution accuracy and convergence speed, as well as to treat constrained optimization problems with discrete design variables. In EFA, modified formulas of interactive forces are used to increase the diversification of the population, and the constraint violations are embedded into the charges of all electromagnetic fireflies to avoid becoming trapped in unfeasible domains. A mechanism called “current-to-best” electromagnetic movement is incorporated with traditional interactive movements to balance the exploration and the exploitation abilities of the EFA. In the local search phase, a newly bi-directional searching procedure is performed on the best firefly to intensify effectively its local optimum. In order to guarantee the convergence capability of the EFA, a harmonized selection mechanism combined with the traditional and elitist selections is proposed and applied if the algorithm cannot find a better optimal solution during a number of predetermined optimization loops. In addition, in this study, the FA and EM with some improvements in several phases are also proposed to solve the constrained optimization problems. Finally, a rounding technique is applied to the proposed EFA, FA and EM for solving discrete optimization problems. The improved performance of the EFA in comparison with the FA, EM as well as other optimization algorithms in the literature is demonstrated by six popular truss optimization problems with discrete variables.

Detection of breast abnormalities in digital mammograms using the electromagnetism-like algorithm

The detection of abnormalities in the breast at an early stage can be so helpful for breast cancer treatment. Currently, mammography is the cheapest and the most efficient technique in terms of identifying the suspicious lesions in the breast. However, the interpretation of this screening remains so hard and could lead to inaccurate detection known as false positive and false negative. Dense breast mammograms particularly are difficult to read because they may contain abnormal structures that are similar to the normal breast tissue. In this paper, we introduce an effective method for the detection of the ambiguous areas in digital mammograms. After noise and artifacts removal from the images using 2D Median filtering and labeling, we isolate the abnormalities using the meta-heuristic algorithm Electromagnetism-like Optimization (EML). The segmentation was carried on the abnormal cases of two different databases Mini-Mias and DDSM. The accuracy detection rate achieves almost 85% for both databases and 91.07% for DDSM alone.

Utilizing new approaches to address the fuzzy fixed charge transportation problem

The Fuzzy Fixed Charge Transportation Problem in which both fixed and transportation cost are fuzzy numbers is considered in this paper. Due to NP-hardness of the problem, we utilize three types of Electromagnetism-like Algorithms (EM), Genetic Algorithm (GA), and Simulated Annealing (SA) which are firstly being proposed and comprised in this research area. Besides, our other novelty approach is the use of new encoding mechanism, namely string representation, for the first time which is employed for the problem and can be used in any extended transportation problems. Also, the last version of EM is being firstly developed and proposed in this paper. The employed operators and parameters are calibrated to ensure the best performance of the algorithms. Besides, different problem sizes are considered at random to study the impacts of the rise in the problem size on the performance of the algorithms.

An Electromagnetism-like Algorithm to Solve Three-dimensional Highway Alignment Problem

Road alignment design is an important determinant of the development cost of road networks. On the one side, it affects road construction and maintenance costs, which constitute a significant part of country-wide infrastructure development, management and operation budget each year. On the other side, it affects road user-related costs of travel time and vehicle use, which comprise a significant portion of the total transportation cost. This study adapts the existing Electromagnetism-like meta-heuristic algorithm to solve a three-dimensional highway alignment problem, which explores and finds a good route between two given points on a terrain. It detects the potentials of the given initial routes, which are enhanced and shaped toward better positions by the help of the local and global search. The final good solution is, then, fine-tuned for a better alignment. Several example problems are designed to show the behavior of the algorithm. The results show that the algorithm satisfactorily maneuvers to by-pass obstacles, and build highway structures where necessary. The set of the example problems in this paper may also serve to found a basis for evaluation of the performance of alternative algorithms

Multi-period project portfolio selection under risk considerations and stochastic income

This paper deals with multi-period project portfolio selection problem. In this problem, the available budget is invested on the best portfolio of projects in each period such that the net profit is maximized. We also consider more realistic assumptions to cover wider range of applications than those reported in previous studies. A novel mathematical model is presented to solve the problem, considering risks, stochastic incomes, and possibility of investing extra budget in each time period. Due to the complexity of the problem, an effective meta-heuristic method hybridized with a local search procedure is presented to solve the problem. The algorithm is based on genetic algorithm (GA), which is a prominent method to solve this type of problems. The GA is enhanced by a new solution representation and well selected operators. It also is hybridized with a local search mechanism to gain better solution in shorter time. The performance of the proposed algorithm is then compared with well-known algorithms, like basic genetic algorithm (GA), particle swarm optimization (PSO), and electromagnetism-like algorithm (EM-like) by means of some prominent indicators. The computation results show the superiority of the proposed algorithm in terms of accuracy, robustness and computation time. At last, the proposed algorithm is wisely combined with PSO to improve the computing time considerably.

Meta-heuristic approaches for fixed-charge solid transportation problem in two-stage supply chain network

This paper introduces a mathematical model for fixed-charge solid transportation problem for a two-stage supply chain network, considering simultaneously both variable and fixed costs. Existence of the fixed costs and thereupon NP-hardness of the problem, use of meta-heuristics is necessary. Therefore, three approaches as genetic algorithm, electromagnetism-like algorithm and charged system search in accordance with priority-based encoding are developed. Contrary to previous researches which considered conveyances for one stage supply chain, here, conveyances are considered in each stage of the supply chain network for the first time. To determine the parameters’ levels and the introduced operators of the algorithms that exhibit best solution, a Taguchi experimental design method is applied. This experimental design decreases the required number of experiments. As final experiment, the performance of the proposed algorithms are compared with each other by running them with different problem sizes.