A Hybrid Particle Swarm Optimization – Simulated Annealing Algorithm for the Probabilistic Travelling Salesman Problem

Cabrera, Guillermo; Roncagliolo, D Silvana; Riquelme, Juan P.; Cubillos, Claudio; Soto, Ricardo

doi:10.24846/v21i1y201206

Cited by 14 publications

(11 citation statements)

References 24 publications

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“…The method is inspired by thermodynamic systems where concepts such as energy, state and temperature are adapted to make it works in combinatorial optimization framework. The algorithm has been extensively studied and applied on a wide range of complex combinatorial optimization problems [13,14,15].…”

Section: Simulated Annealingmentioning

confidence: 99%

Improving Simulated Annealing Performance by means of Automatic Parameter Tuning

Cabrera-Guerrero¹,

Guerrero²,

Johnson³

2015

SIC

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Abstract:A common problem when using (meta)-heuristic techniques to solve complex combinatorial optimization problems is related to parameters tuning. Finding "the right" parameter values can lead to significant improvements in terms of best solution objective value found by the heuristic, heuristic reliability and heuristic convergence, among others. Unfortunately, this is usually a tedious and complicated task if done manually. In this paper, we propose a framework that is based on Genetic Programming to fine-tune a key parameter of the well-known Simulated Annealing (SA) algorithm. Experiments on a set of small instances of the Facility Location Problem with capacity constraints are performed. Results show that automatically adjusting a key parameter in SA by means of Genetic Programming leads to an average value of the obtained solution that is closer to the optimal solution than the average value obtained by the simple SA algorithm with a priori selected values. More important, standard deviation of the algorithm is greatly improved by our approach which makes it much more reliable if time limitations are imposed.

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Section: Simulated Annealingmentioning

confidence: 99%

Improving Simulated Annealing Performance by means of Automatic Parameter Tuning

Cabrera-Guerrero¹,

Guerrero²,

Johnson³

2015

SIC

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“…Lemma 1 [14] The columns which decline monotonically and have lower bound, there must be limits, and lim inf{ } n n n T T →∞ = .…”

Section: The Global Convergence Analysismentioning

confidence: 99%

“…In order to test the effectiveness of improved FFA, We choose Eil51 which is in TSPLIB international standard library as a test function to compare with FFA and improved particle swarm optimization (MPSO) in literature [14].…”

Section: The Global Convergence Analysismentioning

confidence: 99%

An improvement of fruit fly optimization algorithm for solving traveling salesman problems

Chen

Huang

et al. 2014

2014 IEEE International Conference on Information and Automation (ICIA)

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TSP (Traveling Salesman Problem, TSP) is a classic combinatorial optimization problem and nowadays it is a hot topic to find a high precision algorithm with short solving time. As its search space increases with the number of cities, it often requires a lot of computation time to find the optimal solution in a large space. So a new TSP solving algorithm is proposed based on an improved fruit fly optimization algorithm. Aiming at solving these shortcomings of standard fruit fly optimization algorithm, such as easily plunging into local optimal and low convergence-rate, mutation operator is introduced, which improves the diversity of the population and prevents premature. The strategy of adaptive variable step size is adopted, which increases search efficiency effectively. Finally, the improved fruit fly optimization algorithm is verified to be efficient, comparing with standard fruit fly optimization algorithm and particle swarm optimization algorithm benchmarked against TSPLIB, Index Terms -fruit fly optimization algorithm; mutation operator; adaptive variable step size; TSP.

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“…Although the PSO algorithm was initially designed to tackle continuous optimisation problems, during the last decade, it has proven to be a very good alternative for solving combinatorial optimisation problems. In fact, several articles have used this technique to solve complex combinatorial optimisation problems; see, for example, [38,39]. For a comprehensive analysis of publications concerning PSO approaches, see [40].…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

Solving a Novel Inventory Location Model with Stochastic Constraints and(R,s,S)Inventory Control Policy

Cabrera

Miranda

Cabrera

et al. 2013

Mathematical Problems in Engineering

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We solve a novel inventory-location model with a stochastic capacity constraint based on a periodic inventory control (ILM-PR) policy. The ILM-PR policy implies several changes with regard to other previous models proposed in the literature, which consider continuous review as their inventory policy. One of these changes is the inclusion of the undershoot concept, which has not been considered in previous ILM models in the literature. Based on our model, we are able to design a distribution network for a two-level supply chain, addressing both warehouse location and customer assignment decisions, whilst taking into consideration several aspects of inventory planning, in particular, evaluating the impact of the inventory control review period on the network configuration and system costs. Because the model is a very hard-to solve combinatorial nonlinear optimisation problem, we implemented two heuristics to solve it, namely, Tabu Search and Particle Swarm Optimisation. These approaches were tested over small instances in which they were able to find the optimal solution in just a few seconds. Because the model is a new one, a set of medium-size instances is provided that can be useful as a benchmark in future research. The heuristics showed a good convergence rate when applied to those instances. The results confirm that decision making over the inventory control policy has effects on the distribution network design.

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A Hybrid Particle Swarm Optimization – Simulated Annealing Algorithm for the Probabilistic Travelling Salesman Problem

Cited by 14 publications

References 24 publications

Improving Simulated Annealing Performance by means of Automatic Parameter Tuning

Improving Simulated Annealing Performance by means of Automatic Parameter Tuning

An improvement of fruit fly optimization algorithm for solving traveling salesman problems

Solving a Novel Inventory Location Model with Stochastic Constraints and(R,s,S)Inventory Control Policy

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