An adaptive membrane algorithm for solving combinatorial optimization problems

He, Jifeng; Xiao, Jianhua; Shao, Zehui

doi:10.1016/s0252-9602(14)60090-4

Cited by 16 publications

(6 citation statements)

References 28 publications

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“…For the other parameters PS and NS of MEAMVC, we set PS to 5 and NS to 6, which is based on preliminary experiments. For PS, we test PS ∈ [5,20] with an increment step of 5. We find MEAMVC with a PS of 5 performs best.…”

Section: B Results and Analysismentioning

confidence: 99%

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MEAMVC: A Membrane Evolutionary Algorithm for Solving Minimum Vertex Cover Problem

Guo¹,

Quan

Chen³

2019

IEEE Access

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Since the membrane algorithm was proposed, it has been used for many optimization problems such as, traveling salesman problem, the knapsack problem, and so on. In membrane algorithms, the membranes have two functions: container and comparator. As a container, each membrane contains one evolutionary algorithm like genetic algorithm and ant colony algorithm. These algorithms are called sub-algorithms and used to evolve individuals. As a comparator, the membrane will compare the results of sub-algorithms, and select the best as the base of the next evolvement. This paper proposes a novel evolutionary algorithm called membrane evolutionary algorithm framework (MEAF). Unlike the presented membrane algorithms, the membranes in MEAF will be evolved to solve problems by using four operators that are abstracted from the life cycle of living cells. Based on MEAF, a membrane evolutionary algorithm called MEAMVC is proposed to solve the minimum vertex cover (MVC) problem. The experimental results show the advantages of MEAMVC when MEAMVC is compared with two state-of-the-art MVC algorithms proposed in recent years.

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Section: B Results and Analysismentioning

confidence: 99%

“…Another novel membrane algorithm based on the hierarchical structure and particle swarm optimization (PSO) solved the broadcasting problems in [19]. In [20], an adaptive membrane algorithm combining the hierarchical structure and local search was proposed to solve the travelling salesman problem.…”

Section: Introductionmentioning

confidence: 99%

MEAMVC: A Membrane Evolutionary Algorithm for Solving Minimum Vertex Cover Problem

Guo¹,

Quan

Chen³

2019

IEEE Access

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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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“…Compute the Euclidean distance between all solutions and divide them into p + 1 groups [7]. In this way, the diversity of each subpopulation is increased.…”

Section: -Step 2: Divide Initial Populationmentioning

confidence: 99%

“…The dynamic behaviors of membrane algorithms, including diversity and convergency, were also analyzed in [18]. Looking for dynamic behaviors of membrane algorithms, an adaptive membrane algorithm was introduced in [7]. Inspired by the way the biological neuron cells store information, membrane algorithms with a ring structure was proposed for solving the knapsack problem [8].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Distribution Algorithm Based on Membrane Computing for Solving the Multiobjective Multiple Traveling Salesman Problem

Zhang²

2015

Fundamenta Informaticae

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The multiobjective multiple traveling salesman problem (MmTSP), in which multiple salesmen and objectives are involved in a route, is known to be NP-hard. The MmTSP is more appropriate for real-life applications than the classical traveling salesman problem (TSP), however it has not received the same amount of attention. Due to the high complexity of the MmTSP, a promising algorithm for solving it must be based on a global search procedure. This paper proposes a hybrid global search algorithm, that belongs to the membrane computing framework. The search behavior of the algorithm is enhanced by a communication mechanism. The multichromosome representation is considered to reduce the excess runtime. The effectiveness of the proposed algorithm is tested on the MmTSP with disparately-scaled objective functions, salesmen and problem sizes. The experimental results are compared with the NSGA-II and several evolutionary strategies with decomposition, including simulated annealing algorithm, hill climbing method, pure evolutionary algorithm, and genetic algorithm.

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An adaptive membrane algorithm for solving combinatorial optimization problems

Cited by 16 publications

References 28 publications

MEAMVC: A Membrane Evolutionary Algorithm for Solving Minimum Vertex Cover Problem

MEAMVC: A Membrane Evolutionary Algorithm for Solving Minimum Vertex Cover Problem

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

A Hybrid Distribution Algorithm Based on Membrane Computing for Solving the Multiobjective Multiple Traveling Salesman Problem

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