A discrete gravitational search algorithm for solving combinatorial optimization problems

Dowlatshahi, Mohammad Bagher; Nezamabadi‐pour, Hossein; Mashinchi, M.

doi:10.1016/j.ins.2013.09.034

Cited by 81 publications

(31 citation statements)

References 28 publications

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“…For example, one might divide metaheuristics into two categories depending on whether they are pure or hybrid. Examples of pure metaheuristics for the TSP include Simulated Annealing (Kirkpatrick et al, 1983;Malek et al, 1989), Tabu Search (Malek, 1988;Malek et al, 1989;Tsubakitani and Evans, 1998a), Guided Local Search (Voudouris and Tsang, 1999), Jump Search (Tsubakitani and Evans, 1998b), Randomized Priority Search (DePuy, Moraga and Whitehouse, 2005), Greedy Heuristic with Regret (Hassin and Keinan, 2008), Genetic Algorithms (Jayalakshmi et al, 2001;Tsai et al, 2003;Albayrak and Allahverdi, 2011;Nagata and Soler, 2012), Evolutionary Algorithms (Liao et al, 2012), Ant Colony Optimization (Dorigo and Gambardella, 1997), Artificial Neural Networks (Leung et al, 2004;Li et al, 2009), Water Drops Algorithm (Alijla et al, 2014), Discrete Firefly Algorithm (Jati et al, 2013), Invasive Weed Optimization (Zhou et al, 2015), Gravitational Search (Dowlatshahi et al, 2014), and Membrane Algorithms (He et al, 2014). Examples of hybrid metaheuristics include Simulated Annealing with Learning (Lo and Hsu, 1998), Genetic Algorithm with Learning (Liu and Zeng, 2009), SelfOrganizing Neural Networks and Immune System (Masutti and de Castro, 2009), Genetic Algorithm and Local Search (Albayrak and Allahverdi, 2011), Genetic Algorithm and Ant Colony Optimization (Dong at al., 2012), Honey Bees Mating and GRASP (Marinakis et al, 2011), and Particle Swarm Optimization and Ant Colony Optimization (Elloumi et al, 2014).…”

Section: Heuristic Approaches and Methodsmentioning

confidence: 99%

A dual local search framework for combinatorial optimization problems with TSP application

Ouenniche

Ramaswamy

Gendreau

2017

Journal of the Operational Research Society

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Abstract:In practice, solving realistically sized combinatorial optimization problems (COPs) to optimality is often too time-consuming to be affordable; therefore, heuristics are typically implemented within most applications software. A specific category of heuristics has attracted considerable attention, namely, local search methods. Most local search methods are primal in nature; that is, they start the search with a feasible solution and explore the feasible space for better feasible solutions. In this research, we propose a dual local search method and customise it to solve the traveling salesman problem (TSP); that is, a search method that starts with an infeasible solution, explores the dual space -each time reducing infeasibility, and lands in the primal space to deliver a feasible solution. The proposed design aims to replicate the designs of optimal solution methodologies in a heuristic way. To be more specific, we solve a combinatorial relaxation of a TSP formulation, design a neighborhood structure to repair such an infeasible starting solution, and improve components of intermediate dual solutions locally. Sample-based evidence along with statistically significant t-tests support the superiority of this dual design compared to its primal design counterpart.

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Section: Heuristic Approaches and Methodsmentioning

confidence: 99%

A dual local search framework for combinatorial optimization problems with TSP application

Ouenniche

Ramaswamy

Gendreau

2017

Journal of the Operational Research Society

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“…GSA falls also under the category of metaheuristics as general search strategies that, at the exploitation stage, exploit areas of the solution space with high quality solutions and, at the exploration stage, move to unexplored areas of the solution space [97]. GSA is a stochastic population-based metaheuristic that was originally designed for solving continuous optimization problems.…”

Section: 48mentioning

confidence: 99%

“…A recent work on a Discrete Gravitational Search Algorithm (DGSA) to solve combinatorial optimization problems [99] can be found in [97].…”

Section: 48mentioning

confidence: 99%

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

Holzinger

Palade

Rabadán

et al. 2014

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

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Abstract. Evolutionary Algorithms (EAs) are a fascinating branch of computational intelligence with much potential for use in many application areas. The fundamental principle of EAs is to use ideas inspired by the biological mechanisms observed in nature, such as selection and genetic changes, to find the best solution for a given optimization problem. Generally, EAs use iterative processes, by growing a population of solutions selected in a guided random search and using parallel processing, in order to achieve a desired result. Such population based approaches, for example particle swarm and ant colony optimization (inspired from biology), are among the most popular metaheuristic methods being used in machine learning, along with others such as the simulated annealing (inspired from thermodynamics). In this paper, we provide a short survey on the state-of-the-art of EAs, beginning with some background on the theory of evolution and contrasting the original ideas of Darwin and Lamarck; we then continue with a discussion on the analogy between biological and computational sciences, and briefly describe some fundamentals of EAs, including the Genetic

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“…Previous works have revealed the effectiveness and efficiency of the GSA as a global optimizer in solving various continuous problems, its binary version, BGSA, in solving binary encoded problems and its discrete version, DGSA, in solving combinatorial problems (Rashedi et al, 2010(Rashedi et al, , 2011Chatterjee and Mahanti, 2010;Yin et al, 2011;Sarafrazi et al, 2011;Bahrololoum et al, 2012;Li and Zhou, 2011;Taghipour et al, 2010;Dowlatshahi et al, 2014;Yazdani et al, 2014;Doraghinejad et al, 2014;Nezamabadi-pour, 2012, 2013;Rashedi et al, 2013). The advantages of GSA are the capability to escape from local optima and being easy to implement.…”

Section: Introductionmentioning

confidence: 99%

A discrete gravitational search algorithm for solving combinatorial optimization problems

Cited by 81 publications

References 28 publications

A dual local search framework for combinatorial optimization problems with TSP application

A dual local search framework for combinatorial optimization problems with TSP application

Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining

A quantum-inspired gravitational search algorithm for binary encoded optimization problems

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