Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems

Mavrovouniotis, Michalis; Müller, Felipe Martins; Yang, Shengxiang

doi:10.1109/tcyb.2016.2556742

Cited by 187 publications

(96 citation statements)

References 53 publications

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“…Hence, a local search operator can significantly improve the quality of the output. However, the integration of local search in SI algorithms has to be done in such a way that does not significantly increase the computation time or waste evaluations [35].…”

Section: Discussionmentioning

confidence: 99%

“…Researchers view their algorithms from different perspectives in TMO [32]. Some researchers pay more attention on extreme behaviours of a system, in particular, the best that the system can do, e.g., modified offline performance [33,17], collective mean fitness [34], best before change [35,36]. Differently, other researchers want to observe "how close to the moving optimum a solution found by an algorithm is" [37,38].…”

Section: Measurementsmentioning

confidence: 99%

“…In addition the diversity is enhanced with the triggered random immigrants entering the memory, whenever all ants stored in the memory are identical. Recently, another memetic-based ACO was proposed in [35] that uses a local search whenever a new best solution is found. Whenever the algorithm is entering stagnation behaviour (very often because of the local search), the pheromone trails are re-initialized to the maximum value to maintain diversity.…”

Section: Hybridizationsmentioning

confidence: 99%

See 2 more Smart Citations

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

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479

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Discussionmentioning

confidence: 99%

Section: Measurementsmentioning

confidence: 99%

Section: Hybridizationsmentioning

confidence: 99%

See 1 more Smart Citation

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

Self Cite

479

213

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show abstract

“…As introduced in Section IV-A, making use of deterministic algorithms such as branching is not a good choice to seek the optimal solution of this problem. As population-based stochastic algorithms, evolutionary algorithms (EAs), such as PSO, differential evolution (DE), and ACS, are widely applied to address NP-hard problems [36]- [38]. In this paper, ACS is applied to optimize the offloading decision.…”

Section: E Upper Level Optimizationmentioning

confidence: 99%

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Huang

Wang

et al. 2020

IEEE Trans. Cybern.

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This paper studies a multi-user cooperative mobile edge computing offloading (CoMECO) system in a multi-user interference environment, in which delay-sensitive tasks may be executed on local devices, cooperative devices, or the primary MEC server. In this system, we jointly optimize the offloading decision and computation resource allocation for minimizing the total energy consumption of all mobile users under the delay constraint. If this problem is solved directly, the offloading decision and computation resource allocation are generally generated separately at the same time. Note, however, that they are closely coupled. Therefore, under this condition, their dependency is not well considered, thus leading to poor performance. We transform this problem into a bilevel optimization problem, in which the offloading decision is generated in the upper level, and then the optimal allocation of computation resources is obtained in the lower level based on the given offloading decision. In this way, the dependency between the offloading decision and computation resource allocation can be fully taken into account. Subsequently, a bilevel optimization approach, called BiJOR, is proposed. In BiJOR, candidate modes are first pruned to reduce the number of infeasible offloading decisions. Afterward, the upper level optimization problem is solved by ant colony system (ACS). Furthermore, a sorting strategy is incorporated into ACS to construct feasible offloading decisions with a higher probability and a local search operator is designed in ACS to accelerate the convergence. For the lower level optimization problem, it is solved by the monotonic optimization method. In addition, BiJOR is extended to deal with a complex scenario with the channel selection. Extensive experiments are carried out to investigate the performance of BiJOR on two sets of instances with up to 400 mobile users. The experimental results demonstrate the effectiveness of BiJOR and the superiority of the CoMECO system.

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“…The main aim of TSP is to obtain the shortest tour that starts from one city and visits each of the other cities once before returning to the starting city. This is one of the most fundamental NPcomplete optimization problems [2]. Since the TSP problem belongs to the class of NP complete problems, its solution grows exponentially with the increase in distribution points.…”

Section: Introductionmentioning

confidence: 99%

Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems

Zoua

Chen

2017

MATEC Web Conf.

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Abstract. In this paper, a discrete variant of TLBO (DTLBO) is proposed for solving the traveling salesman problem (TSP). In the proposed method, an effective learner representation scheme is redefined based on the characteristics of TSP problem. Moreover, all learners are randomly divided into several sub-swarms with equal amounts of learners so as to increase the diversity of population and reduce the probability of being trapped in local optimum. In each subswarm, the new positions of learners in the teaching phase and the learning phase are generated by the crossover operation, the legality detection and mutation operation, and then the offspring learners are determined based on greedy selection. Finally, to verify the performance of the proposed algorithm, benchmark TSP problems are examined and the results indicate that DTLBO is effective compared with other algorithms used for TSP problems.

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Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems

Cited by 187 publications

References 53 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A Bilevel Optimization Approach for Joint Offloading Decision and Resource Allocation in Cooperative Mobile Edge Computing

Discrete Teaching-learning-based optimization Algorithm for Traveling Salesman Problems

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