Metaheuristic algorithms and probabilistic behaviour: a comprehensive analysis of Ant Colony Optimization and its variants

Abstract

The application of metaheuristic algorithms to combinatorial optimization problems is on the rise and is growing rapidly now than ever before. In this paper the historical context and the conducive environment that accelerated this particular trend of inspiring analogies or metaphors from various natural phenomena are analysed. We have implemented the Ant System Model and the other variants of ACO including the 3-Opt, Max-Min, Elitist and the Rank Based Systems as mentioned in their original works and we converse the missing pieces of Dorigo's Ant System Model. Extensive analysis of the variants on Travelling Salesman Problem and Job Shop Scheduling Problem shows how much they really contribute towards obtaining better solutions. The stochastic nature of these algorithms has been preserved to the maximum extent to keep the implementations as generic as possible. We observe that stochastic implementations show greater resistance to changes in parameter values, still obtaining near optimal solutions. We report how Polynomial Turing Reduction helps us to solve Job Shop Scheduling Problem without making considerable changes in the implementation of Travelling Salesman Problem, which could be extended to solve other NPHard problems. We elaborate on the various parallelization options based on the constraints enforced by strong scaling (fixed size problem) and weak scaling (fixed time problem). Also Electronic supplementary material The online version of this article (doi:10.1007/s10462-015-9441-y) contains supplementary material, which is available to authorized users.The code is licensed and made available under the open source Creative Commons License to enable constructive criticism, reproducible research and to ensure maximum dissemination of the research work. The code, results and graphs are provided as supplementary materials which will also be made available as a project in Google code and Git-Hub. B Anandkumar PrakasamA. Prakasam, N. Savarimuthu we elaborate on how probabilistic behaviour helps us to strike a balance between intensification and diversification of the search space.

II-Learn—A Novel Metric for Measuring the Intelligence Increase and Evolution of Artificial Learning Systems

“…A Min-Max Ant System (MMAS) [97,111,112] is an ACO, a variant of the AS. MMAS have some differences from the AS in some aspects.…”

“…We considered a simple rote-learning approach, where ILS BL simply copies the behavior of a MMAS [97,111,112]. After learning, the obtained multiagent system is denoted as ILS AL .…”

Many paths to the same goal: metaheuristic operation of brains during natural behavior

“…Indeed, varieties of animals solve difficult optimization problems nearly instantaneously 10,11,12,13 . Metaheuristics, where an agent selects a heuristic to solve a difficult optimization problem especially when information is imperfect, are a promising computational framework that could support such complex problem solving 14,15 . However, it is not known whether animals use this type of reasoning when solving challenging optimization problems.…”

Maximized Users Influence in Social Networks by using Hybrid Modified Firefly with Ant Colony Optimization Algorithm

“…Prakasam, A., et al [6] Despite the fact that a great deal must be done in building up an exhaustive metaheuristic structure, the nature of metaheuristic algorithms must be protected and the stochastic conduct which is utilized to demonstrate the combination of such algorithms ought not be messed with. Likewise from the dialogs it is evident that examinations of metaheuristic algorithms ought not be performed without thinking about the factual hugeness, the nature and execution subtleties of the likelihood conveyance capacities utilized in the exploratory reproductions.…”