Many optimization problems in various fields have been solved using diverse optimization al gorithms. Traditional optimization techniques such as linear programming (LP), non-linear programming (NLP), and dynamic program ming (DP) have had major roles in solving these problems. However, their drawbacks generate demand for other types of algorithms, such as heuristic optimization approaches (simulated annealing, tabu search, and evolutionary algo rithms). However, there are still some possibili ties of devising new heuristic algorithms based on analogies with natural or artificial phenom ena. A new heuristic algorithm, mimicking the improvisation of music players, has been devel oped and named Harmony Search (HS). The performance of the algorithm is illustrated with a traveling salesman problem (TSP), a specific academic optimization problem, and a least-cost pipe network design problem.
An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach...
A stochastic approach called the annealing-genetic algorithm is presented for solving some well-known combinatorial optimization problems. This approach incorporates genetic alg...
The aim of this paper is to evaluate the performance of genetic algorithms for the flowshop scheduling problem with an objective of minimizing the makespan. First we examine var...
This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents calle...
Natural evolution provides a paradigm for the design of stochastic-search optimization algorithms. Various forms of simulated evolution, such as genetic algorithms and evolution...
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