This study proposes a new metaheuristic algorithm, called "Geometric Octal Zones Distance Estimation" (GOZDE) algorithm to solve global optimization problems. The presented GOZDE employs a search scheme with...
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This study proposes a new metaheuristic algorithm, called "Geometric Octal Zones Distance Estimation" (GOZDE) algorithm to solve global optimization problems. The presented GOZDE employs a search scheme with the information sharing between the zones considering the distance of the zones utilizing median values. The whole population represents the eight zones that are the combination of different search strategies to guide knowledge dissemination from one zone to others in the search space. To demonstrate the effectiveness of the proposed optimizer, it is compared with two classes of metaheuristics, which are (1) GA, PSO, DE, CS and HS as the classical metaheuristics and (2) BWO, SSA, MVO, HHO, ChOA, AOA and EBOwithCMAR as the up-to-date metaheuristics. The search capability of the proposed algorithm is tested on two different numerical benchmark sets including low and high dimensional problems. The developed algorithm is also adapted to ten realworld applications to handle constraint optimization problems. In addition, to further analyse the results of the proposed algorithm, three well-known statistical metrics, Friedman, Wilcoxon ranksum and Whisker-Box statistical tests are conducted. The experimental results statistically show that GOZDE is significantly better than, or at least comparable to the twelve metaheuristic algorithms with outstanding performance in solving numerical functions and real-world optimization problems. (C) 2022 Elsevier B.V. All rights reserved.
Patient admission scheduling (PAS) is a tasking combinatorial optimization problem where a set of patients is assigned to limited facilities such as rooms, timeslots, and beds subject to satisfying a set of predefined...
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Patient admission scheduling (PAS) is a tasking combinatorial optimization problem where a set of patients is assigned to limited facilities such as rooms, timeslots, and beds subject to satisfying a set of predefined constraints. The investigations into the performance of population-based algorithms that utilized to tackle the PAS problem considered in this paper reveal their weaknesses in obtaining quality solutions that create a space to investigate the performance of another population-based method. Thus, in this paper, an Artificial Bee Colony Algorithm (ABC) is proposed to tackle the formulation of the PAS problem under consideration. It is a class of swarm intelligence metaheuristic algorithms based on the intelligent foraging behaviour of honey bees developed to solve continuous and complex optimization problems. Due to the discretization of the PAS, the continuous nature of the ABC algorithm is changed to cope with the rugged solution space of the PAS. The initial feasible solution to the PAS problem is obtained using the room-oriented approach. Then the ABC algorithm optimizes the feasible solutions with the aid of three neighbourhood structures embedded within the employed bee and the onlooker bee operators of the algorithm. The performance of the proposed ABC algorithm based on three different parameters, the solution number (SN), limit value (LV), and the maximum cycle number (MCN) is evaluated on six standard benchmark datasets of the PAS. Two of these main parameters (i.e. SN and LV) are fine-tuned to obtain the best solutions on instances like Test-data 1 = 679.80, Test-data 2 = 1180.40, Test-data 3 = 787.40, Test-data 4 = 1198.60, Test-data 5 = 636.80, and Test-data 6 = 818.60. The best solutions obtained by the proposed method are evaluated against the results of the 19 comparative algorithms comprising five population-based methods, eleven heuristic, and hyperheuristic-basedmethods, and three integer programming-basedmethods. The proposed method
In this paper, we propose a memetic approach to deal with routing problem with capacity and time constraints. Our approach aims to determine least-cost trips for a homogenous fleet of vehicles that serve a set of geog...
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ISBN:
(纸本)9783031162107;9783031162091
In this paper, we propose a memetic approach to deal with routing problem with capacity and time constraints. Our approach aims to determine least-cost trips for a homogenous fleet of vehicles that serve a set of geographically dispersed customers with deterministic demands. Customers time constraints and vehicle capacity have to be rigorously respected during the solving process. Our approach is based on a bio-inspired stochastic algorithm that mimics the reaction process of chemical molecules, which interact until attaining a stable state with minimum free energy. Through the sequence of elementary reactions, molecules explore different regions of the solution space toward the lowest energy state. Since the effectiveness of the optimization process mainly depends on the quality of initial population, we integrated a two-step procedure involving greedy randomized construction with local search. In order to assess our approach, we present a wide variety of computational experiments. The obtained results are compared to those of the best metaheuristics. These results confirm the effectiveness and good performances of our approach.
Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Dif...
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Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population-based method: The Teaching Learning based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching-learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1 degrees.
The amount of different products and services obtained from forests depends on several management decisions such as thinning years, thinning intensity, thinning type, and rotation length. The relationships between man...
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The amount of different products and services obtained from forests depends on several management decisions such as thinning years, thinning intensity, thinning type, and rotation length. The relationships between management actions and the various outputs obtained from forests are complicated. This makes stand management optimization challenging, especially if the number of simultaneously maximized outputs and the number of optimized variables are high. The direct search method of Hooke and Jeeves (HJ) has been used much in stand management optimization. In recent years, population-based methods have been proposed as an alternative to the HJ method. The performance of a population-based method depends on its parameters such as number iterations and population size (number of solution vectors used in the population-based method). This study used two-level meta optimization to simultaneously optimize the parameters of a population-based method and the management schedule of a stand. Four population-based methods were analysed: differential evolution (DE), particle swarm optimization (PS), evolution strategy optimization (ES), and the method of Nelder and Mead (NM). With optimal parameter values, DE and PS found the best stand management schedules, followed by ES and NM. DE and PS performed better than HJ. Therefore, DE and PS should be used more in forest management and their search algorithms should be further developed.
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