Ant colony optimization theory: a survey
Theoretical Computer Science
Solution bias in ant colony optimisation: Lessons for selecting pheromone models
Computers and Operations Research
Ant Colony Optimization Algorithms for Shortest Path Problems
Network Control and Optimization
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A novel ant colony optimization algorithm in application of pheromone diffusion
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part II
A hybrid method for learning Bayesian networks based on ant colony optimization
Applied Soft Computing
Two-stage updating pheromone for invariant ant colony optimization algorithm
Expert Systems with Applications: An International Journal
A novel ACO algorithm with adaptive parameter
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Study of parametric relation in ant colony optimization approach to traveling salesman problem
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
A method for avoiding the feedback searching bias in ant colony optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Countering the negative search bias of ant colony optimization in subset selection problems
Computers and Operations Research
A method for avoiding the searching bias in ACO deceptive problem solving
Web Intelligence and Agent Systems
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One of the problems encountered when applying ant colony optimization (ACO) to combinatorial optimization problems is that the search process is sometimes biased by algorithm features such as the pheromone model and the solution construction process. Sometimes this bias is harmful and results in a decrease in algorithm performance over time, which is called second-order deception. In this work, we study the reasons for the occurrence of second-order deception. In this context, we introduce the concept of competition-balanced system (CBS), which is a property of the combination of an ACO algorithm with a problem instance. We show by means of an example that combinations of ACO algorithms with problem instances that are not CBSs may suffer from a bias that leads to second-order deception. Finally, we show that the choice of an appropriate pheromone model is crucial for the success of the ACO algorithm, and it can help avoid second-order deception.