Heuristics based on greedy randomized adaptive search and variable neighbourhood search for the minimum labelling spanning tree problem

This paper studies heuristics for the minimum labelling spanning tree (MLST) problem. The purpose is to find a spanning tree using edges that are as similar as possible. Given an undirected labelled connected graph, the minimum labelling spanning tree problem seeks a spanning tree whose edges have the smallest number of distinct labels. This problem has been shown to be NP-complete. A Greedy Randomized Adaptive Search Procedure (GRASP) and different versions of Variable Neighbourhood Search (VNS) are proposed. They are compared with other algorithms recommended in the literature: the Modified Genetic Algorithm and the Pilot Method. Nonparametric statistical tests show that the heuristics based on GRASP and VNS outperform the other algorithms tested. Furthermore, a comparison with the results provided by an exact approach shows that we may quickly obtain optimal or near-optimal solutions with the proposed heuristics

OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives

A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. 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Microsoft robotics soccer challenge : movement optimization of a quadruped robot

Estágio realizado na Universidade de Aveiro e orientado pelo Prof. Doutor Nuno LauTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Efficient GRASP+VND and GRASP+VNS metaheuristics for the traveling repairman problem

The traveling repairman problem is a customer-centric routing problem, in which the total waiting time of the customers is minimized, rather than the total travel time of a vehicle. To date, research on this problem has focused on exact algorithms and approximation methods. This paper presents the first metaheuristic approach for the traveling repairman problem

Optimal design of steel¿concrete composite bridge based on a transfer function discrete swarm intelligence algorithm

[EN] Bridge optimization can be complex because of the large number of variables involved in the problem. In this paper, two box-girder steel¿concrete composite bridge single objective optimizations have been carried out considering cost and CO¿ emissions as objective functions. Taking CO¿ emissions as an objective function allows adding sustainable criteria to compare the results with cost. SAMO2, SCA, and Jaya metaheuristics have been applied to reach this goal. Transfer functions have been implemented to fit SCA and Jaya to the discontinuous nature of the bridge optimization problem. Furthermore, a Design of Experiments has been conducted to tune the algorithm and set its parameters. Consequently, it has been observed that SCA shows similar values for objective cost function as SAMO2 but improves computational time by 18% while also getting lower values for the objective function result deviation. From a cost and CO¿ optimization analysis, it has been observed that a reduction of 2.51 kg CO¿ is obtained by each euro reduced using metaheuristic techniques. Moreover, for both optimization objectives, it is observed that adding cells to bridge cross-sections improves not only the section behavior but also the optimization results. Finally, it is observed that the proposed design of double composite action in the supports allows this study to remove continuous longitudinal stiffeners in the bottom flange.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been made possible thanks to funding received from the following research projects: Grant PID2020-117056RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe", Grant FPU-18/01592 funded by MCIN/AEI/10.13039/501100011033 and by "ESF invests in your future" and Grant CONICYT/FONDECYT/INICIACION/11180056.Martínez-Muñoz, D.; García, J.; Martí Albiñana, JV.; Yepes, V. (2022). Optimal design of steel¿concrete composite bridge based on a transfer function discrete swarm intelligence algorithm. Structural and Multidisciplinary Optimization. 65(11):1-25. https://doi.org/10.1007/s00158-022-03393-9125651

A search algorithm for constrained engineering optimization and tuning the gains of controllers

In this work, the application of an optimization algorithm is investigated to optimize static and dynamic engineering problems. The methodology of the approach is to generate random solutions and find a zone for the initial answer and keep reducing the zones. The generated solution in each loop is independent of the previous answer that creates a powerful method. Simplicity as its main advantage and the interlaced use of intensification and diversification mechanisms--to refine the solution and avoid local minima/maxima--enable the users to apply that for a variety of problems. The proposed approach has been validated by several previously solved examples in structural optimization and scored good results. The method is also employed for dynamic problems in vibration and control. A modification has also been done on the method for high-dimensional test functions (functions with very large search domains) to converge fast to the global minimum or maximum; simulated for several well-known benchmarks successfully. For validation, a number of 9 static and 4 dynamic constrained optimization benchmark applications and 32 benchmark test functions are solved and provided, 45 in total. All the codes of this work are available as supplementary material in the online version of the paper on the journal website

Optimal Design of Steel Towers Using a Multi-Metaheuristic Based Search Method

In meta-heuristic algorithms, the problem of parameter tuning is one of the most important issues that can be highly time consuming. To overcome this difficulty, a number of researchers have improved the performance of their methods by enhancement and hybridization with other algorithms. In the present paper efforts are made to search design space simultaneously by the Multi Metaheuristic based Search Method (MMSM). In the proposed method, optimization process is performed by dividing the initial population into five subsets so-called islands. An improved multi-metaheuristic method is then employed. After a certain number of repetitions (migration intervals), some percent of the island’s best members are transferred into another island (migration) and replaced by the members of low fitnesses. In the migration phase, the target island is chosen randomly. Examples of large design spaces are utilized to investigate the efficiency of the proposed method. For this purpose, steel are optimized utilizing the proposed method. The results indicate improvements in the available responses