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Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and fields. Several metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), grey wolf optimization algorithm (GWO), genetic algorithm (GA), and gravitational search algorithm (GSA). According to the literature, no one metaheuristic optimization algorithm can handle all present optimization problems. Hence novel optimization methodologies are still needed. The Al-Biruni earth radius (BER) search optimization algorithm is proposed in this paper. The proposed algorithm was motivated by the behavior of swarm members in achieving their global goals. The search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method. A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER's validation and testing against seven mathematical optimization problems. The results show that BER can both explore and avoid local optima. BER has also been tested on an engineering design optimization problem. The results reveal that, in terms of performance and capability, BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

We consider knowledge-oriented models, problems, and algorithms for routing traveling salesmen in complex networks. The formalization leads to models of pseudo-Boolean discrete optimization with constraints that take into account the specifics of the multiple traveling salesmen problem. We consider a class of problems that can be represented in the form of pseudo-Boolean optimization models with separable objective functions (monotone, linear) and constraints in the form of disjunctive normal forms (DNFs). We demonstrate the possibility of an approximate synthesis of DNF constraints based on precedent information. The methodology, theoretical principles, and algorithms for solving problems of this class are presented. It is shown that the solution of routing problems can be based on the application of a multiagent approach in combination with clustering of the original problem, pseudo-Boolean optimization algorithms with disjunctive constraints, and metaheuristics. [ABSTRACT FROM AUTHOR]

When we face an optimization problem whose de finition (in some aspect) changes over the time, we are in the presence of a Dynamic Optimization Problem (DOP). The aspects that can change are the objective function, the variables' domain, the appearance/disappearance of variables or constraints, etc. This paper aims at providing a first introduction to those who are interested in the topic. Concretely, we present the DOPs, the most common performance measures as well as the methods used to solve them. We also briefly describe some of the most recent reviews and comment some current challenges and research opportunities in DOPs. [ABSTRACT FROM AUTHOR]

• A new variant for SCSO for solving the problem of slow convergence and late exploitation. • Updating sand cat agent positions inspired by the political system. • Providing broader searches in the search area based on the cross-party membership switching mechanism. • Better than other well-known algorithms in four different problem types. • Has suitable behavior in exploration and exploitation phases. The Sand Cat Swarm Optimization (SCSO) algorithm is a recently introduced metaheuristic with balanced behavior in the exploration and exploitation phases. However, it is not fast in convergence and may not be successful in finding the global optima, especially for complex problems since it starts the exploitation phase late. Moreover, the performance of SCSO is also affected by incorrect position as it depends on the location of the global optimum. Therefore, this study proposes a new method for the SCSO algorithm with a multidisciplinary principle inspired by the Political (Parliamentary) system, which is named PSCSO. The suggested algorithm increases the chances of finding the global solution by randomly choosing positions between the position of the candidate's best solution available so far and the current position during the exploitation phase. In this regard, a new coefficient is defined that affects the exploration and exploitation phases. In addition, a new mathematical model is introduced to use in the exploitation phase. The performance of the PSCSO algorithm is analyzed on a total of 41 benchmark functions from CEC2015, 2017, and 2019. In addition, its performance is evaluated in four classical engineering problems. The proposed algorithm is compared with the SCSO, Stochastic variation and Elite collaboration in SCSO (SE-SCSO), Hybrid SCSO (HSCSO), Parliamentary Optimization Algorithm (POA), and Arithmetic Optimization Algorithm (AOA) algorithms, which have been proposed in recent years. The obtained results depict that the PSCSO algorithm performs better or equivalently to the compared optimization algorithms. [ABSTRACT FROM AUTHOR]

Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires the definition of an optimization framework whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecting the optimization and segmentation algorithms’ parameters; choosing the representation for encoding prior knowledge on the image domain of interest; and initializing the curve in a location which favors its convergence onto the boundary of the object of interest. All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques that are generally termed metaheuristics, and are designed to solve complex optimization and machine learning problems. In particular, we present a complete study on the application of metaheuristics to image segmentation based on deformable models. This survey studies, analyzes and contextualizes the most notable and recent works on this topic, proposing an original categorization for these hybrid approaches. It aims to serve as a reference work which proposes some guidelines for choosing and designing the most appropriate combination of deformable models and metaheuristics when facing a given segmentation problem. After recalling the principles underlying deformable models and metaheuristics, we broadly review the different hybrid approaches employed to solve image segmentation problems, and conclude with a general discussion about methodological and design issues as well as future research and application trends. [ABSTRACT FROM AUTHOR]

We consider in this paper a double loading problem (DLP), an NP-hard optimization problem of extreme economic relevance in industrial areas. The problem consists in loading items into bins, then stowing bins in a set of compartments. The main objective is to minimize the number of used bins. We state the mathematical model as well as a modified binary particle swarm optimization with FFD initialization that outperforms state-of-the-art approaches carried out on a large testbed instances. [ABSTRACT FROM AUTHOR]

This paper deals with how to efficiently deploy energy-harvesting Relay Nodes in previously established low-cost static Wireless Sensor Networks (WSNs), assuming a single-tiered network model. The purpose is to optimise three conflicting objectives: Average Energy Cost, Average Sensitivity Area, and Network Reliability. This is the so-called Relay Node Placement Problem (RNPP), which is an NP-hard optimisation problem. We find many works assuming heuristics in the current literature. However, it is not the case for metaheuristics, which usually provide good results solving such complex problems. This situation led us to consider a wide range of MultiObjective (MO) metaheuristics: the two standard Genetic Algorithms NSGA-II and SPEA2, the trajectory algorithm MO-VNS, the algorithm based on decomposition MOEA/D, and two novel swarm intelligence algorithms MO-ABC and MO-FA, which are based on the behaviour of honey bees and fireflies, respectively. These metaheuristics are applied to optimise a freely available data set. The results obtained are analysed considering two MO metrics: hypervolume and set coverage. Through a widely accepted statistical methodology, we conclude that MO-FA provides the best performance on average. We also study the efficiency of this approach, verifying that it is a good strategy to optimise such networks, including some limitations. Finally, we compare this proposal to another author approach, which assumes a heuristic. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies the auto-tuning of shared-memory hyperheuristics developed on top of a unified shared-memory metaheuristic scheme. A theoretical model of the execution time of the unified scheme is empirically adapted for particular metaheuristics and hyperheuristics through experimentation. The model is used to decide at running time the number of threads to obtain a reduced execution time. The number of threads is different for the different basic functions in the scheme, and depends on the problem to be solved, the metaheuristic scheme, the implementation of the basic functions and the computational system where the problem is solved. The applicability of the proposal is shown with a problem of minimization of electricity consumption in exploitation of wells. Experimental results show that satisfactory execution times can be achieved with auto-tuning techniques based on theoretical-empirical models of the execution time. [Copyright &y& Elsevier]