The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

<span lang="EN-US">Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies </span><span lang="EN-US">using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection. </span>

Section: Introductionmentioning

confidence: 99%

Adapted Branch-and-Bound Algorithm Using SVM With Model Selection

Kabbaj¹,

Abdellatif²

2019

“…HMMs success is due to ability to deal with the variability by means of stochastic modeling. It was used to enhance the behavior of metaheuristics by estimating their best configuration [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

Limam¹,

Afia²,

Chiheb³

2018

Self Cite

Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.

“…In [13] authors proposed an evolved Ant Colony System algorithm by dynamically adapting the local pheromone decay parameter using fuzzy logic controller. The inputs for their fuzzy system are the same as in Olivas proposed method.…”

mentioning

confidence: 99%

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

Bouzbita

Afia

Faizi

2020

Self Cite

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails 𝜉 and 𝜌 using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters 𝜉 and 𝜌 on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter 𝜉 is more effective compared to the standard ACS.