We propose a hybrid algorithm based on the Ant Colony Optimization (ACO) meta-heuristic, in conjunction with four well-known elimination rules, to tackle the NP -hard single-machine scheduling problem to minimize the total job tardiness. The hybrid algorithm has the same running time as that of ACO. We conducted extensive computational experiments to test the performance of the hybrid algorithm...

In this chapter the authors address a hybrid flow shop scheduling problem considering the minimization of the makespan in addition to the sum of earliness and tardiness penalties. This problem is proven to be NP-hard, and consequently the development of heuristic and meta-heuristic approaches to solve it is well justified. So, to deal with this problem, the authors propose a method which consis...

ARTICLE 1. Introduction Computational Grids (CGs) are a natural extension of classical distributed systems. CGs are expected to leverage unprecedented larger computing capacities by virtually joining together geographically distributed resources at large scale. To achieve this objective, the design of ecient Grid schedulers that map and allocate tasks and applications onto Grid resources is a k...

the capacitated clustering problem (ccp) is one of the most importantcombinational optimization problems that nowadays has many real applications inindustrial and service problems. in the ccp, a given n nodes with known demandsmust be partitioned into k distinct clusters in which each cluster is detailed by anode acting as a cluster center of this cluster. the objective is to minimize the sumof...

abstract— in this paper we have tried to develop an altered version of the artificial bee colony algorithm which is inspired from and combined with the meta-heuristic algorithm of firefly. in this method, we have tried to change the main equation of searching within the original abc algorithm. on this basis, a new combined equation was used for steps of employed bees and onlooker bees. for this...

This paper addresses a rollon–rolloff waste collection vehicle routing problem which involves large containers that collect huge amounts of waste from construction sites and shopping malls. In this problem, vehicles could only move one container at a time between customers’ locations, a depot, a disposal center, and a recycling center site. Other than a disposal center, a recycling ...

Designing the architecture and correct parameters for the learning algorithm is a tedious task for modeling an optimal Artificial Neural Network (ANN), which is smaller, faster and with a better generalization performance. In this paper we explain how a hybrid algorithm integrating Genetic algorithm (GA), Simulated Annealing (SA) and other heuristic procedures can be applied for the optimal des...

Local search is an integral part of many meta-heuristic strategies that solve single objective optimisation problems. Essentially, the meta-heuristic is responsible for generating a good starting point from which a greedy local search will find the local optimum. Indeed, the best known solutions to many hard problems (such as the travelling salesman problem) have been generated in this hybrid w...

Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as: GA, PSO, ACO, SA and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR f...