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Hybridization of metaheuristic algorithms has recently been introduced to increase the search capabilities of a traditional metaheuristic algorithms. The fuzzy optimization approach (FOA) is a metaheuristic algorithm that implements fuzzy logic systems to incorporate the knowledge and experience of an expert metaheuristic designer. This integration enables the FOA to guide the search strategy throughout the optimization process. It has been widely applied in high-dimensional problems due to its robustness. However, it presents some issues, such as a slow convergence rate, low exploration and exploitation mechanisms, and high computational effort. In this paper, hybrid search mechanisms are implemented to the original structure of FOA to increase its performance in terms of search capabilities over the limits of the fitness functions. The proposed method called ODM-FOA combines the advantages of Metropolis Hasting (MH) initialization, opposition-based learning (OBL), and Diversity measures to correctly identify and register prominent areas within the search space by approximating the fitness function and improving the exploration and exploitation of the search space. A comparison between ODM-FOA and the original FOA is implemented. Additionally, to evaluate the performance of the developed scheme, it is compared among six well-known metaheuristic algorithms on a set of benchmark functions. Experimental results demonstrate the effectiveness and robustness of the proposed approach against the other original FOA and the other methodologies in terms of solution quality, dimensionality, similarity, and convergence criteria. [ABSTRACT FROM AUTHOR]

• In this article, an optimal evaluation of re-opening policies for COVID-19 is developed. • The method provides effective re-opening policies using agent-based models and the whale optimization algorithm. • Agent-based models simulate the transmission risk in scenarios that are impossible to evaluate in real conditions. • The whale optimization algorithm is used to find the best scenario where the lowest risk of infection is reached. • Experiments demonstrate that the proposed approach delivers realistic predictions to formulate re-opening policies. A new contagious disease or unidentified COVID-19 variants could provoke a new collapse in the global economy. Under such conditions, companies, factories, and organizations must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the Whale Optimization Algorithm is used to find the solution with the minimal transmission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and essential estimations for identifying optimal re-opening strategies with the lowest transmission risk. [ABSTRACT FROM AUTHOR]

Most metaheuristic methods employ a strategy designed to be generical and fixed. As a result, these methods often lack the capability to adaptively modify their performance in response to different scenarios or challenges encountered during the search process. This paper presents a new metaheuristic algorithm designed to dynamically adjust its search strategy throughout the optimization process for increased efficiency. This algorithm is based on Evolutionary Strategies (ES) due to their notable self-adaptive features. To further enhance its efficiency, we have incorporated elements from Evolutionary Game Theory (EGT). This integration ensures a more comprehensive strategy adaptation process, taking into account not only the information from the specific agent but also insights from other population members. Additionally, our approach alters the conventional EGT mechanism by including not just pairwise evaluations but also data from the top-performing individuals in the population, based on their outcomes. This broader adaptation strategy allows for a faster convergence to the most effective dominant strategy. To demonstrate the effectiveness of our method, we compared it against several established metaheuristic algorithms using 28 diverse test functions. Our findings reveal that this approach produces competitive results, delivering higher-quality solutions and faster convergence rates. [ABSTRACT FROM AUTHOR]

Optimization is a very important issue to efficiently solve engineering problems. The clear comprehension of the elements and processes of optimizer algorithms is fundamental to ensure that engineer students are capable to apply, tune, and design new algorithms. This paper presents an educational platform based on LEGO© EV3 and MATLAB to assist the learning of the principles of classical and metaheuristic optimization algorithms at undergraduate level, by providing a simple and easy-to-follow teaching setup. The proposed study aims to accompany students through the learning of optimization fundamentals by building hands-on robotic experiments. The proposed educational platform has been successfully applied to several undergraduate courses within the Electronics Department at the University of Guadalajara. The description of each experiment and the evaluation of their impact in the student performance are both provided in the paper. [ABSTRACT FROM AUTHOR]

Economic Order Quantity (EOQ) is an important optimization problem for inventory management with an impact on various industries; however, their mathematical models may be complex with non-convex, non-linear, and non-differentiable objective functions. Metaheuristic algorithms have emerged as powerful tools for solving complex optimization problems (including EOQ). They are iterative search techniques that can efficiently explore large solution spaces and obtain near-optimal solutions. Simulated Annealing (SA) is a widely used metaheuristic method able to avoid local suboptimal solutions. The traditional SA algorithm is based on a single agent, which may result in a low convergence rate for complex problems. This article proposes a modified multiple-agent (population-based) adaptive SA algorithm; the adaptive algorithm imposes a slight attraction of all agents to the current best solution. As a proof of concept, the proposed algorithm was tested on a particular EOQ problem (recently studied in the literature and interesting by itself) in which the objective function is non-linear, non-convex, and non-differentiable. With these new mechanisms, the algorithm allows for the exploration of different regions of the solution space and determines the global optimum in a faster manner. The analysis showed that the proposed algorithm performed well in finding good solutions in a reasonably short amount of time. [ABSTRACT FROM AUTHOR]

This paper presents a novel and effective technique for extracting multiple ellipses from an image. The approach employs an evolutionary algorithm to mimic the way animals behave collectively assuming the overall detection process as a multi-modal optimization problem. In the algorithm, searcher agents emulate a group of animals that interact with each other using simple biological rules which are modeled as evolutionary operators. In turn, such operators are applied to each agent considering that the complete group has a memory to store optimal solutions (ellipses) seen so far by applying a competition principle. The detector uses a combination of five edge points as parameters to determine ellipse candidates (possible solutions), while a matching function determines if such ellipse candidates are actually present in the image. Guided by the values of such matching functions, the set of encoded candidate ellipses are evolved through the evolutionary algorithm so that the best candidates can be fitted into the actual ellipses within the image. Just after the optimization process ends, an analysis over the embedded memory is executed in order to find the best obtained solution (the best ellipse) and significant local minima (remaining ellipses). Experimental results over several complex synthetic and natural images have validated the efficiency of the proposed technique regarding accuracy, speed, and robustness. [ABSTRACT FROM AUTHOR]

Interest in multimodal function optimization is expanding rapidly as real-world optimization problems often demand locating multiple optima within a search space. This article presents a new multimodal optimization algorithm named as the collective animal behavior. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents are a group of animals which interact with each other based on the biologic laws of collective motion. Experimental results demonstrate that the proposed algorithm is capable of finding global and local optima of benchmark multimodal optimization problems with a higher efficiency in comparison with other methods reported in the literature. [ABSTRACT FROM AUTHOR]

A vital subject of engineering structures is mechanical oscillations. If the mechanical oscillation is uncontrolled, it can lead to structural failure due to large dynamic stresses developed, as the collapse occurred on the Broughton Suspension bridge due to soldiers walking in step. This paper addresses mechanical oscillation problems employing a proposed modified variant of coyote optimizer. Coyote optimization algorithm (COA) is a new meta-heuristic which mimics the social behavior of coyotes. COA suffers with stagnation problems and immature convergence while solving optimization problems. In this paper, the COA is hybridized with Laplace Crossover operator and new culture tendency strategies are adapted, modified variants of coyote optimization algorithm (MvCOA). The proposed MvCOA is presented to approximately solve mechanical oscillation problems independently of their order, form, and stated conditions. With the fundamental concepts of ordinary differential equations and Fourier series expansion, mechanical oscillation problems can be modeled as a problem of optimization whereby the optimization task is achieved using the proposed MvCOA. Five different ordinary differential equations and four mechanical oscillation problems are solved approximately and then compared with their corresponding exact solutions. The statistical analysis validates that the presented MvCOA is an effective algorithm for different optimization problems. However, from the empirical results, it is visible that the suggested MvCOA approximate approach was able to reach a successful performance solving different mechanical oscillation problems. [ABSTRACT FROM AUTHOR]

A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions. [ABSTRACT FROM AUTHOR]

Supplier selection and order quantity allocation have a strong influence on a company's profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances. [ABSTRACT FROM AUTHOR]

Model-based testing is an automated process in which executable tests are derived from behavioral models of a system. Model checking is a verification technique to reveal errors in which all reachable states of a system can be generated as state space. In the literature, different approaches suggest using model checkers for model-based testing. Model checker explores all possible system states, so utilizing the various paths in the state-space as test cases seems a promising solution. However, these approaches suffer from two main challenges. The first challenge is state space explosion, which prevents generating all reachable states by the model checker. The second one is generating redundant test cases. Recently, several methods using meta-heuristic and evolutionary approaches have been proposed to cope with these problems. Therefore, exploring a portion of state space using an optimization approach to detect the test objectives can be a proper way to manage the state space explosion and generate an optimal test suite with the least redundancy. In this paper, a method is proposed using a Bayesian optimization algorithm (BOA), and a model checker is as a bed to generate test cases for the service-oriented systems. In the proposed approach, the test suite is a set of paths on the state space starting from an initial state and leading to the state in which all the test objectives are satisfied. In this research, we have implemented BOA with three different structures in GROOVE toolset, an open-source toolset for designing and model checking graph transformation. Experimental results show that our solution generates better results in terms of coverage and speed in different case studies than the existing approaches. [ABSTRACT FROM AUTHOR]

• A modified version of the Grey Wolf Optimizer (GWO) is introduced. • In the proposed approach, a synchronous-asynchronous scheme is adopted. • Our approach also uses a diversity operation. • With its inclusion, our method allows to solve highly multimodal problems. • Computational results demonstrate that the proposed approach presents competitive results. Grey Wolf Optimizer represents a relatively new metaheuristic scheme for solving continuous optimization problems. In spite of its interesting characteristics, it presents several flaws such as lack of accuracy, low diversity, premature convergence and imbalance between exploitation and exploration. In this paper, a modified version of the Grey Wolf Optimizer called Group-based Synchronous-Asynchronous Grey Wolf Optimizer is introduced. The proposed scheme incorporates a synchronous-asynchronous processing scheme, a set of different nonlinear functions and an operation to increase diversity. With such mechanisms, the proposed algorithm presents a better balance between exploration and exploitation, an increment in the accuracy and the ability to avoid the convergence in local minima. Such capacities allow its use in complex engineering problems that involve highly multimodal objective functions with a difficult localization of their global optimum. To evaluate the performance of the proposed approach, it has been tested on a representative number of functions of the well-known IEEE Congress on Evolutionary Computation 2017 benchmark set of functions and real-world engineering problems. The results of our method have been compared against those produced by other states of the art metaheuristic algorithms. The results prove the effectiveness and accuracy of the proposed technique. [ABSTRACT FROM AUTHOR]

Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several approaches have been proposed in the literature based on spatial and fr uency domain techniques. Among them, the Incomplete Beta Function (IBF) is a popular scheme used for image contrast enhancement. In the IBF based image contrast enhancement technique, quality of an image is improved by two controlling parameters. Under such conditions, these parameters need to be tuned for obtaining better outcomes. In this paper, a new gray-scale contrast enhancement algorithm is introduced where, instead of tuning the controlling parameters of IBF experimentally, their near-optimal values are calculated with the help of a recently published meta-heuristic algorithm called Artificial Electric Field Algorithm (AEFA). The proposed method has been compared with many state-of-the-art techniques in terms of some standard metrics considering three different datasets, namely Kodak, MIT-Adobe FiveK and USC-SIPI. Simulation results demonstrate that the proposed AEFA based image enhancement technique increases the overall image contrast and enriches the information present in the image. [ABSTRACT FROM AUTHOR]

In the past years, the interest in direct current to direct current converters has increased because of their application in renewable energy systems. Consequently, the research community is working on improving its efficiency in providing the required voltage to electronic devices with the lowest input current ripple. Recently, a hybrid converter which combines the boost and the Cuk converter in an interleaved manner has been introduced. The converter has the advantage of providing a relatively low input current ripple by a former strategy. However, it has been proposed to operate with dependent duty cycles, limiting its capacity to further decrease the input current ripple. Independent duty cycles can significantly reduce the input current ripple if the same voltage gain is achieved by an appropriate duty cycle combination. Nevertheless, finding the optimal duty cycle combination is not an easy task. Therefore, this article proposes a new pulse-width-modulation strategy for the hybrid interleaved boost-Cuk converter. The strategy includes the development of a novel mathematical model to describe the relationship between independent duty cycles and the input current ripple. The model is introduced to minimize the input current ripple by finding the optimal duty cycle combination using the differential evolution algorithm. It is shown that the proposed method further reduces the input current ripple for an operating range. Compared to the former strategy, the proposed method provides a more balanced power-sharing among converters. [ABSTRACT FROM AUTHOR]

The natural aggregation algorithm (NAA) is a new efficient population-based optimizer. The NAA has a competent performance when compared to other well-established optimizers. However, a problem of concern is NAA lack of exploitation in its local search. In this article, we propose an improved version of NAA. The modifications made are: hypercubes with displacement and shrink mechanism applied in each shelter, we designed a new movement operator to search inside the hypercubes, an improved readjustment of the algorithm's parameters and "leave shelter" formula of NAA, to better mimic the aggregation behavior. To prove the effectiveness of the modified hypercube natural aggregation algorithm (HYNAA), we compared with classics optimizers, such as PSO, DE and ABC, state of the art, such as CMA-ES, MSA and NAA himself with a benchmark of 28 functions. The said functions consist of five unimodal, 19 multimodal and four hybrids, and we compared them on 30, 50 and 100 dimensions. We also made extra comparisons against NAA in 500 and 1000 dimensions to contrast the ability of the hypercubes to reduce the dimensional complexity. Finally, we tested two trajectory optimization problems. Experimental results and statistical tests demonstrate that the performance of HYNAA is significantly better than that of other optimizers. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal methodology based on the Differential Evolution algorithm for obtaining the set of duty cycles of a recently proposed power electronics converter with input current ripple cancelation capability. The converter understudy was recently introduced to the state-of-the-art as the interleaved connection of two unequal converters to achieve low input current ripple. A latter contribution proposed a so-called proportional strategy. The strategy can be described as the equations to relate the duty cycles of the unequal power stages. This article proposes a third switching strategy that provides a lower input current ripple than the proportional strategy. This is made by considering duty cycles independently of each other instead of proportionally. The proposed method uses the Differential Evolution algorithm to determine the optimal switching pattern that allows high quality at the input current side, given the reactive components, the switching frequency, and power levels. The mathematical model of the converter is analyzed, and thus, the decision variables and the optimization problem are well set. The proposed methodology is validated through numerical experimentation, which shows that the proposed method achieves lower input current ripples than the proportional strategy. [ABSTRACT FROM AUTHOR]

Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by several scientific communities. Despite their interesting properties, sensors present an adverse characteristic: they manage very limited energy. Under such conditions, saving energy represents one of the most important concepts in designing effective protocols for WSNs. The objective of a protocol is to increase the network lifetime through the reduction of energy consumed by each sensor. In this paper, a robust clustering routing protocol for WSNs is introduced. The scheme uses the Locust Search (LS-II) method to determine the number of cluster heads and to identify the optimal cluster heads. Once the cluster heads are recognized, the other sensor elements are assigned to their nearest corresponding cluster head. Numerical simulations exhibit competitive results and demonstrate that the proposed protocol allows for the minimization of the energy consumption, extending the network lifetime in comparison with other popular clustering routing protocols. [ABSTRACT FROM AUTHOR]

As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment. [ABSTRACT FROM AUTHOR]

This manuscript presents the numerical optimization (through a mathematical model and an evolutionary algorithm) of the voltage-doubler boost converter, also called the series-capacitor boost converter. The circuit is driven by two transistors, each of them activated according to a switching signal. In the former operation, switching signals have an algebraic dependence from each other. This article proposes a new method to operate the converter. The proposed process reduces the input current ripple without changing any converter model parameter, only the driving signals. In the proposed operation, switching signals of transistors are independent of each other, providing an extra degree of freedom, but on the other hand, this produces an infinite number of possible combinations of duty cycles (the main parameter of switching signals) to achieve the desired voltage gain. In other words, this leads to a problem with infinite possible solutions. The proposed method utilizes an evolutionary algorithm to determine the switching functions and, at the same time, to minimize the input current ripple of the converter. A comparison made between the former and the proposed operation shows that the proposed process achieves a lower input current ripple while achieving the desired voltage gain. [ABSTRACT FROM AUTHOR]