This study proposes an Ant Colony Optimization using Genetic Information (GIACO). The GIACO algorithm combines Ant Colony Optimization (ACO) with Genetic Algorithm (GA). GIACO searches solutions by using the pheromone of ACO and the genetic information of GA. In addition, two kinds of ants coexist: intelligent ant and dull ant. The dull ant is caused by the mutation and cannot trail the pheromo...

Ant colony optimization (ACO) is a technique for optimization that was introduced in the early 1990’s. The inspiring source o f 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 and continuous optimization problems and to important problems in telecommunications, such a...

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A deterministic model for Ant Colony Optimization (ACO) algorithms is proposed and used to study the dynamics of ACO. The model is based on the average expected behaviour of ants. The behaviour of ACO algorithms and the model are analysed for certain types of permutation problems. It is shown numerically that decisions of the ants are inuenced in an intriguing way by the properties of the phero...

Images which are captured in outdoor scenes often degrade in quality due to atmospheric conditions induced by suspending particles like mist, haze, fog, etc. which scatter and absorb light before it reaches to the camera. The weather-degraded images directly affect the efficiency of computerized surveillance and monitoring system. In this paper, we propose an efficient haze removal technique wh...

The present article focuses on the application of a procedure based on ant colonies to solve an assembly line balancing problem. After an introduction to assembly line problems, the problem under study is presented: the Time and Space constrained Assembly Line Balancing Problem (TSALBP); and a basic model of one of its variants is put forward for study. Subsequently, an ant algorithm is present...

The ant colony optimisation metaheuristic has shown promise on simplified artificial instances of university course timetabling problems. However, limited work has been done applying it to practical timetabling problems. In this paper, we describe the application of the ant colony optimisation to a highly constrained real–world instance of the university course timetabling problem. We present t...

This paper mainly addresses the balance control of a ball and beam system, where a pair of decoupled fuzzy sliding-mode controllers (DFSMCs) are proposed. The DFSMC has the advantage of reducing the design complexity, in which the coupling dynamics of the state-error dynamics are considered as disturbance terms. Stability analysis of the ball and beam system with DFSMCs is also discussed in det...

This paper investigates a comparative study which addresses the P/prec/Cmax scheduling problem, a notable NP-hard benchmark. MLP_SACS, a modified ant colony algorithm, is used to solve it. Its application provides us a better job allocation to machines. In front of each machine, the jobs are performed with three priority rules, the longest path (LP), a modified longest path (MLP) and a maximum ...