An improved electromagnetism-like mechanism algorithm for constrained optimization

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.

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.

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.

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.

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.

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

A NEW HEURISTIC-EM FOR PERMUTATION FLOWSHOP SCHEDULING

This paper presents the use of a constraint-handling technique, known as feasibility and dominance rules, in a electromagnetism-like (ELM) mechanism for solving constrained global optimization problems. Since the original ELM algorithm is specifically designed for solving bound constrained problems, only the inequality and equality constraints violation together with the objective function value are used to select points and to progress towards feasibility and optimality. Numerical experiments are presented, including a comparison with other methods recently reported in the literature.

For calibrating the conceptual hydrological models (CHM), the traditional calibration method with a single objective cannot properly measure all the behaviors of the hydrological system. To obtain a successful parameters calibration, in this paper, we propose a multi-objective cultural self-adaptive electromagnetism-like mechanism (MOCSEM) algorithm, which is first implemented in solving the parameters calibration problem of CHM. In this algorithm, a self-adaptive parameter is applied in local search operation for adjusting the values of parameters dynamically. Meanwhile, cultural algorithm (CA) is adopted to keep a good diversity and uniformity of Pareto-optimal solutions (POS). MOCSEM is tested, firstly, by several benchmark test problems. After achieving satisfactory performance on the test problems, a case study is implemented for parameter calibration of a CHM by comparing the properties of POS obtained by the MOCSEM and other methods. Finally, when the optimization problem quickly becomes a decision-making problem because of the multiple objectives in CHM, fuzzy technique for order preference by similarity to an ideal solution method has been used to rank the POS and select the optimal scheme. The results show that the MOCSEM algorithm can provide high-accuracy parameters of CHM on various decision-making scenarios.

OBVIOUSLY A PROBLEM in scientific research must appeal to someone to directors and sponsors as well as to actual workers, in order to be undertaken at all. In order to make it appeal it must be expressed in language and described as a project. Its good possibilities must be emphasized with skill; its disadvantages must be slurred over. To worker it must appear to offer prospects for attainment of prestige, rapid publication of research papers, and advancement in status and income. To scientific or institutional director it must be such as to appear important and must offer such prospects of results and subsequent erudite publications as can be depended upon to impress lay sponsors or legislators, as case may be. Propaganda is needed all along line. Hence we find 0. A. Stevens writing in Science for November 26, 1937 on Freedom of Press and Scientist. Stevens put in a plea for better scientific publications, rather than an increased number of uncertain quality as at present. He said that the chief restriction on scientist's publication is publisher's profit or bank balance of technical society. If editor rejects, author can still publish at his own expense. The consequent avalanche of publication, very largely propaganda, threatens to confuse and engulf conscientious and serious research worker. The multiplication of small scientific journals of overlapping subject matter occurs constantly. Brief notes appear time and again to put certain matters on record and to gain notice, but promised definitive reports never appear. Mimeographing is cheap and is resorted to for purposes of scientific propagandizing. Research workers appreciate this condition, but each one is reluctant to curtail his own propagandizing. Stevens concluded: am quite aware of need of increased facilities for publication, but I am convinced that we have a still greater need for a wiser use of those now available. Scientific publication; being a byproduct of research and not a directly remunerative activity for scientist, has a peculiar status. Essentially scientific specialists subsidize their own publication outlets (in most cases) by paying very high subscription rates for technical journals. Editorial staffs mostly work unpaid. They are for that rcason alone diffi-

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.

This paper describes a multi‐start with clustering strategy for use on constrained optimization problems. It is based on the characteristics of non‐linear constrained global optimization problems and extends a strategy previously tested on unconstrained problems. Earlier studies of multi‐start with clustering found in the literature have focused on unconstrained problems with little attention to non‐linear constrained problems. In this study, variations of multi‐start with clustering are considered including a simulated annealing or random search procedure for sampling the design domain and a quadratic programming (QP) sub‐problem used in cluster formation. The strategies are evaluated by solving 18 non‐linear mathematical problems and six engineering design problems. Numerical results show that the solution of a one‐step QP sub‐problem helps predict possible regions of attraction of local minima and can enhance robustness and effectiveness in identifying local minima without sacrificing efficiency. In comparison to other multi‐start techniques found in the literature, the strategies of this study can be attractive in terms of the number of local searches performed, the number of minima found, whether the global minimum is located, and the number of the function evaluations required. Copyright © 2002 John Wiley & Sons, Ltd.

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

Supply chain efficiency is a major challenge in today’s business environment, where efficient resource allocation and coordination of activities are essential for competitive advantage. Traditional efficiency strategies often struggle for resources for the complex and dynamic network. In response, bio-inspired metaheuristic algorithms have emerged as powerful tools to solve these optimization problems. Referring to the random search nature of metaheuristic algorithms and emphasizing that no metaheuristic algorithm is the best optimizer for all optimization applications, the No Free Lunch (NFL) theorem encourages researchers to design newer algorithms to be able to provide more effective solutions to optimization problems. Motivated by the NFL theorem, the innovation and novelty of this paper is in designing a new meta-heuristic algorithm called Bobcat Optimization Algorithm (BOA) that imitates the natural behavior of bobcats in the wild. The basic inspiration of BOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. The theory of BOA is stated and then mathematically modeled in two phases (i) exploration based on the simulation of the bobcat’s position change while moving towards the prey and (ii) exploitation based on simulating the bobcat’s position change during the chase process to catch the prey. The performance of BOA is evaluated in optimization to handle the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100, as well as to address CEC 2020. The optimization results show that BOA has a high ability in exploration, exploitation, and balance them during the search process in order to achieve a suitable solution for optimization problems. The results obtained from BOA are compared with the performance of twelve well-known metaheuristic algorithms. The findings show that BOA has been successful in handling the CEC 2017 test suite in 89.65, 79.31, 93.10, and 89.65% of the functions for the problem dimension equal to 10, 30, 50, and 100, respectively. Also, the findings show that in order to handle the CEC 2020 test suite, BOA has been successful in 100% of the functions of this test suite. The statistical analysis confirms that BOA has a significant statistical superiority in the competition with the compared algorithms. Also, in order to analyze the efficiency of BOA in dealing with real world applications, twenty-two constrained optimization problems from CEC 2011 test suite and four engineering design problems have been selected. The findings show that BOA has been successful in 90.90% of CEC2011 test suite optimization problems and in 100% of engineering design problems. In addition, the efficiency of BOA to handle SCM applications has been challenged to solve ten case studies in the field of sustainable lot size optimization. The findings show that BOA has successfully provided superior performance in 100% of the case studies compared to competitor algorithms.

This paper proposed a fast convergence and balanced adolescent identity search algorithm (FCBAISA) for numerical and engineering design problems. The main contributions are as follows. Firstly, a hierarchical optimization strategy is proposed to balance the exploration and exploitation better. Secondly, a fast search strategy is proposed to avoid the local optimization and improve the accuracy of the algorithm; that is, the current optimal solution combines with the random disturbance of Brownian motion to guide other adolescents. Thirdly, the Chebyshev functional-link network (CFLN) is improved by recursive least squares estimation (RSLE), so as to find the optimal solution more effectively. Fourthly, the terminal bounce strategy is designed to avoid the algorithm falling into local optimization in the later stage of iteration. Fifthly, FCBAISA and comparison algorithms are tested by CEC2017 and CEC2022 benchmark functions, and the practical engineering problems are solved by algorithms above. The results show that FCBAISA is superior to other algorithms in all aspects and has high precision, fast convergence speed, and excellent performance.