Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Swarm and Evolutionary Computation

2017

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492

213

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.

Section: Discrete Applicationsmentioning

confidence: 99%

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

Swarm and Evolutionary Computation

2017

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492

213

“…This is because many objective functions for such problems may require a lot of time to compute [6], [19]. Hence, using a smaller population size may be appropriate in such situations to reduce the computation time and maintain the solution quality.…”

Section: Discussionmentioning

confidence: 99%

Empirical study on the effect of population size on MAX-MIN ant system in dynamic environments

2016 IEEE Congress on Evolutionary Computation (CEC)

2016

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Abstract-In this paper, the effect of the population size on the performance of the MAX -MIN ant system for dynamic optimization problems (DOPs) is investigated. DOPs are generated with the dynamic benchmark generator for permutationencoded problems. In particular, the empirical study investigates: a) possible dependencies of the population size parameter with the dynamic properties of DOPs; b) the effect of the population size with the problem size of the DOP; and c) whether a larger population size with less algorithmic iterations performs better than a smaller population size with more algorithmic iterations given the same computational budget for each environmental change. Our study shows that the population size is sensitive to the magnitude of change of the DOP and less sensitive to the frequency of change and the problem size. It also shows that a longer duration in terms of algorithmic iterations results in a better performance.

“…Therefore, when a dynamic change occurs the past experience can be transferred via the pheromone trails of previously optimized environments [7]. Successful ACO applications to dynamic combinatorial optimization problems include Internet-like network routing [8], vehicle routing [9] and train scheduling [10].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Algorithms for Dynamic Optimization: A Case Study of the Dynamic Travelling Salesperson Problem [Research Frontier]

IEEE Comput. Intell. Mag.

Van

et al. 2020

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Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This paper investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are classified into two frameworks: evaporation-based and population-based. A case study of using these algorithms to solve the dynamic travelling salesperson problem is described. Experiments are systematically conducted using a proposed dynamic benchmark framework to analyze the effect of important ant colony optimization features on numerous test cases. Different performance measures are used to evaluate the adaptation capabilities of the investigated algorithms, indicating which features are the most important when designing ant colony optimization algorithms in dynamic environments.