Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optim...

ant colony optimisation (aco) algorithm and adaptive refinement mechanism are used in this paper for solution of optimization problems. many of the real engineering problems are، however، of continuous nature and finding their solution by discrete ant based algorithms requires discretisation of the decision variables in which affected the convergence and performance of the algorithm. in this pa...

An industrial ethane thermal cracking reactor was modeled assuming a molecular mechanism for the reaction kinetics coupled with material, energy, and momentum balances of the reactant-product flow along the reactor. To carry out the multi-objective optimization for two objectives such as conversion and ethylene selectivity, the elitist non-dominated sorting genetic algorithm was used. The Paret...

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to produce the phenomenon of stagnation and precocity. This paper introduces improve...

the one-dimensional cutting stock problem, has so many applications in lots of industrial processes and during the past few years has attracted so many researchers’ attention all over the world. in this paper a meta-heuristic method based on aco is presented to solve this problem. in this algorithm, based on designed probabilistic laws, artificial ants do select various cuts and then select the...

this paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. in addition, the setup time of a job on each machin...

In this paper, we present an improved continuous ant colony algorithm for global optimization of continuous functions. We show how to improve the quality of Ant Colony Optimization, ACO, to solve continuous optimization problems. We present the general idea, implementation, the analysis of its convergence and results obtained. We compare the results with Adaptive Genetic Algorithm, AGA, and Con...

In this paper, we propose a dynamic, non-dominated sorting, multiobjective particle-swarm-based optimizer, named Hierarchical Non-dominated Sorting Particle Swarm Optimizer (H-NSPSO), for memory usage optimization in embedded systems. It significantly reduces the computational complexity of others MultiObjective Particle Swarm Optimization (MOPSO) algorithms. Concretely, it first uses a fast no...