Global search in single-solution-based metaheuristics

Abstract

<jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>Metaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>In this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title><jats:p>The proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Originality/value</jats:title><jats:p>In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.</jats:p></jats:sec>

Regional Location Routing Problem for Waste Collection Using Hybrid Genetic Algorithm-Simulated Annealing

“…Single-solution approaches have the advantage of providing better results in terms of quality because of emphasizing an exploitation strategy or focusing on finding optimum solutions around a good (near-)optimal solution [26]. However, the global optimum may not reach and may get stuck in the local optimum due to the randomized initial solution [27]. In contrast, the population-based approach maintains the diversity of the solution globally and has the potency of expanding the search space or exploration strategy, so it has advantages to better search space [26,27].…”

“…However, the global optimum may not reach and may get stuck in the local optimum due to the randomized initial solution [27]. In contrast, the population-based approach maintains the diversity of the solution globally and has the potency of expanding the search space or exploration strategy, so it has advantages to better search space [26,27]. However, the population-based approach requires sufficient knowledge regarding the search space in the initial steps, so the solution quality may be worse than a single solution because more exploitation is required by a lapse of steps [26].…”

Hybrid optimization algorithm based feature selection for mammogram images and detecting the breast mass using multilayer perceptron classifier

“…There are two forms of meta‐heuristics: single‐solution and population‐based approaches. Single‐solution techniques concentrate on changing and enhancing a single potential solution 10 . It includes variable neighborhood search, simulated annealing, iterated and guided local search.…”

“…Single-solution techniques concentrate on changing and enhancing a single potential solution. 10 It includes variable neighborhood search, simulated annealing, iterated and guided local search. Population-based techniques sustain and improve multiple solutions for candidates, frequently with population-based features that direct the research.…”

Dual Kidney-Inspired Algorithm for Water Quality Prediction and Cancer Detection

“…Optimization methods, which are normally provided by metaheuristic algorithms as one of the intelligent system techniques, can optimize Artificial Neural Network (ANN) models. Many researchers have employed metaheuristic algorithms to train ANN models [1][2][3][4][5][6][7][8][9]. Finding the global optima solution is the most important goal of the optimization process.…”