Journal ArticleDOI
Minimizing the multimodal functions with Ant Colony Optimization approach
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This paper presents the ACO-based algorithm that is used to find the global minimum of a nonconvex function, based on that each ant searches only around the best solution of the previous iteration.Abstract:
The ant colony optimization (ACO) algorithms, which are inspired by the behaviour of ants to find solutions to combinatorial optimization problem, are multi-agent systems. This paper presents the ACO-based algorithm that is used to find the global minimum of a nonconvex function. The algorithm is based on that each ant searches only around the best solution of the previous iteration. This algorithm was tested on some standard test functions, and successful results were obtained. Its performance was compared with the other algorithms, and was observed to be better.read more
Citations
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Global optimization and simulated annealing
TL;DR: The mathematical formulation of the simulated annealing algorithm is extended to continuous optimization problems, and it is proved asymptotic convergence to the set of global optima.
Journal ArticleDOI
A memetic particle swarm optimization algorithm for multimodal optimization problems
TL;DR: Experimental results based on a set of benchmark functions show that the proposed memetic algorithm is a good optimizer in multimodal optimization domain.
Journal ArticleDOI
Short Communication: Solving facility layout problems using Flexible Bay Structure representation and Ant System algorithm
Kuan Yew Wong,Komarudin +1 more
TL;DR: An Ant System (AS) algorithm for solving Unequal Area Facility Layout Problems (UA-FLPs) using Flexible Bay Structure (FBS) representation is proposed and encouraging results are obtained.
Journal ArticleDOI
Ant colony optimization-based algorithm for airline crew scheduling problem
Guang-Feng Deng,Woo-Tsong Lin +1 more
TL;DR: Results showed that ACO-based algorithm can be potential technique for airline crew scheduling and perform more effective and robust than Genetic algorithms for airlineCrew scheduling problem.
Journal ArticleDOI
A new solution algorithm for improving performance of ant colony optimization
TL;DR: The results showed that the ACORSES performs better than other optimization algorithms, available in literature in terms of minimum values of objective functions and number of iterations.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Journal ArticleDOI
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Journal ArticleDOI
Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
Proceedings ArticleDOI
Ant colony optimization: a new meta-heuristic
Marco Dorigo,G. Di Caro +1 more
TL;DR: This work defines the Ant Colony Optimization (ACO) meta-heuristic by defining these algorithms in a common framework by defining the foraging behavior of ant colonies as a meta- heuristic.
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