Ant Colony Optimization on a Budget of 1000

Cáceres, Leslie Pérez; López-Ibáñez, Manuel; Stützle, Thomas

doi:10.1007/978-3-319-09952-1_5

Cited by 10 publications

(9 citation statements)

References 14 publications

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“…For the PACO, a fixed number of ants, i.e., p pop = 10, was adapted, as a result of which the number of iterations for each subproblem of the DTSP equaled 0.1 · p ev . This is consistent with the observations that were presented in Cáceres et al (2014), in which the ACO algorithms were tested for a small computational budget. The values of the other parameters for the PACO were: β = 3, q 0 = 0.8, cl = 30, α = 0.1 and ψ = 0.1-pheromone evaporation coefficients for local and global pheromone trail updates, respectively.…”

Section: Parameters Of the Algorithmssupporting

confidence: 91%

Adjustability of a discrete particle swarm optimization for the dynamic TSP

2017

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This paper presents a detailed study of the discrete particle swarm optimization algorithm (DPSO) applied to solve the dynamic traveling salesman problem which has many practical applications in planning, logistics and chip manufacturing. The dynamic version is especially important in practical applications in which new circumstances, e.g., a traffic jam or a machine failure, could force changes to the problem specification. The DPSO algorithm was enriched with a pheromone memory which is used to guide the search process similarly to the ant colony optimization algorithm. The paper extends our previous work on the DPSO algorithm in various ways. Firstly, the performance of the algorithm is thoroughly tested on a set of newly generated DTSP instances which differ in the number and the size of the changes. Secondly, the impact of the pheromone memory on the convergence of the DPSO is investigated and compared with the version without a pheromone memory. Moreover, the results are compared with two ant colony optimization algorithms, namely the MAX -MIN ant system (MMAS) and the population-based ant colony optimization (PACO). The results show that the DPSO is able to find high-quality solutions to the DTSP and its performance is competitive with the performance of the MMAS and the PACO algorithms. Moreover, the pheromone memory has a positive impact on Communicated by V. Loia.

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Section: Parameters Of the Algorithmssupporting

confidence: 91%

Adjustability of a discrete particle swarm optimization for the dynamic TSP

2017

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“…In Knowles et al (2009), a fitness distance-based correlation strategy has been developed for efficient performance of an MOO problem in the presence of noise constrained by a very limited evaluations budget. While such surrogate modeling approaches, capturing the noisy fitness landscapes, have widely been applied to black-box continuous function optimization problems, their application is rare for combinatorial optimization problems (Cáceres et al 2014). …”

Section: Literature Reviewmentioning

confidence: 99%

Non-dominated Sorting Bee Colony optimization in the presence of noise

Rakshit

Konar

2015

Soft Comput

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The paper incorporates new extensional strategies into the traditional multi-objective optimization algorithms to proficiently obtain the Pareto-optimal solutions in the presence of noise in the fitness landscapes. The first strategy, referred to as adaptive selection of sample size, is employed to assess the trade-off between accuracy in fitness estimation and the associated run-time complexity. The second strategy is concerned with determining statistical expectation of fitness samples, instead of their conventional averaging, as the fitness measure of the trial solutions. The third strategy aims at improving Goldberg's approach to examine possible accommodation of a seemingly inferior solution in the optimal Pareto front using a more statistically viable comparator. The traditional Non-dominated Sorting Bee Colony algorithm has been ameliorated by extending its selection step with the proposed strategies. Experiments undertaken to study the performance of the proposed algorithm reveal that the extended algorithm outperforms its contenders with respect to four performance metrics, when examined on a test suite of 23 standard benchmarks with additive noise of three statistical distributions.

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“…The values of the parameters were set based on preliminary computations and the suggestions by Cáceres el al. [27], in which the ACO was tested with a small computation budget. All the considered algorithms, including DPSO and ACO, were allowed to construct and evaluate exactly the same number of solutions (p ev ) to a problem.…”

Section: Comparative Studymentioning

confidence: 99%

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

Strąk¹,

Skinderowicz²,

Boryczka³

et al. 2019

Preprint

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This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic travelling salesman problem (DTSP). The DTSP can be modelled as a sequence of static sub-problems, each of which is an instance of the TSP. We present a method for automatically setting the values of the DPSO parameters without three parameters, which can be defined based on the size of the problem, the size of the particle swarm, the number of iterations, and the particle neighbourhood size. We show that the diversity of parameter values has a positive effect on the quality of the generated results. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

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Ant Colony Optimization on a Budget of 1000

Cited by 10 publications

References 14 publications

Adjustability of a discrete particle swarm optimization for the dynamic TSP

Adjustability of a discrete particle swarm optimization for the dynamic TSP

Non-dominated Sorting Bee Colony optimization in the presence of noise

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

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