Bio-inspired Ant Algorithms: A review

Lecture Notes in Computer Science, 2004

Polaris Global Journal of Scholarly Research and Trends

The complexity in real-world problems motivated researchers to innovate efficient problem-solving techniques. Generally natural Inspired, Bio Inspired, Metaheuristics based on evolutionary computation and swarm intelligence algorithms have been frequently used for solving complex, real-world optimization and Non-deterministic polynomial hard (NP-Hard) problems because of their ability to adjust to variety of conditions. This paper shows an overview for swarm based algorithm that based on ant behavior. The first algorithm that inspired ant behavior in search for food source was developed in 1992 and was tested in solving TSP problem Ant Colony Optimization (ACO) is a metaheuristic inspired by some ant species' pheromone trail laying and following behavior. Artificial ants in ACO are stochastic solution construction processes that use (artificial) pheromone information that is modified depending on the ants' search experience and possibly accessible heuristic information to...

arXiv: Neural and Evolutionary Computing, 2019

Ant Colony Optimization (ACO) is a metaheuristic proposed by Marco Dorigo in 1991 based on behavior of biological ants. Pheromone laying and selection of shortest route with the help of pheromone inspired development of first ACO algorithm. Since, presentation of first such algorithm, many researchers have worked and published their research in this field. Though initial results were not so promising but recent developments have made this metaheuristic a significant algorithm in Swarm Intelligence. This research presents a brief overview of recent developments carried out in ACO algorithms in terms of both applications and algorithmic developments. For application developments, multi-objective optimization, continuous optimization and time-varying NP-hard problems have been presented. While to review articles based on algorithmic development, hybridization and parallel architectures have been investigated.

Ant Colony Optimization Algorithm is a meta-heuristic, multi-agent technique that can be applied for solving difficult NP-Hard Combinatorial Optimization Problems like Traveling Salesman Problem (TSP), Job Shop Scheduling Problem (JSP), Vehicle Routing Problem (VRP) and many more. The Positive Feedback Mechanism and Distributed Computing ability makes it very robust in nature. The artificial ants implement a randomized construction heuristic which makes probabilistic decisions as a function of artificial pheromone trails to solve the problems that are dependent on the input data. In spite of ACO having global searching ability and high convergence speed towards optimal solutions, it has some limitations like low population scattering ability and no systematic way of startup. To overcome these problems, various hybrids of ACO with other algorithms like Dynamic Programming, Genetic Algorithm and Particle Swarm Optimization have been proposed to provide better results than using ACO in...

Swarm intelligence has been successfully applied in various domains. One of the most popular techniques of swarm intelligence uses Ant Colony Optimization (ACO) algorithms to solve continuous or mixed discrete-continuous variable optimization problems. This article starts by formally deriving the evolutionary dynamics of ant colony optimization, an important swarm intelligence algorithm. Ants of the artificial colony are able to generate successively shorter feasible path by using information accumulated in the form of a pheromone trail. Ant colony optimization is already used in too many areas from graph related problems to the medical problem and study of Genomics to communication networks etc.

Wiley Encyclopedia of …, 2011

Ant colony optimization (ACO) [1-3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic information. The pheromone trails are updated during algorithm execution to bias the ants search toward promising decisions previously found. The article titled Ant Colony Optimization gives a detailed overview of the main concepts of ACO.

Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithm for combinational optimization problems. It is a way to solve optimization problems based on the way that ants indirectly communicate directions to each other. The behavior of ants has been documented and the subject of easily writing and fables passed from one century to another century. The successful techniques used by ant colonies have been studied in computer science and robotics to produce distributed and fault tolerance system for solving problems as well as used in fault tolerance storage and networking algorithm. Metaheuristic algorithms are algorithms which, in order to escape from local optima, drive some basic heuristic: either a constructive heuristic, starting from the null solution and adding elements to build a good complete one, or local search heuristic, starting from a complete solution and iteratively modifying some of its elements in order to achieve a better one.

Handbook of metaheuristics, 2003

Physics of Life Reviews, 2005

Ant colony optimization is a technique for optimization that was introduced in the early 1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. First, we deal with the biological inspiration of ant colony optimization algorithms. We show how this biological inspiration can be transfered into an algorithm for discrete optimization. Then, we outline ant colony optimization in more general terms in the context of discrete optimization, and present some of the nowadays bestperforming ant colony optimization variants. After summarizing some important theoretical results, we demonstrate how ant colony optimization can be applied to continuous optimization problems. Finally, we provide examples of an interesting recent research direction: The hybridization with more classical techniques from artificial intelligence and operations research.