one of the most important extensions of the capacitated vehicle routing problem (cvrp) is the vehicle routing problem with simultaneous pickup and delivery (vrpspd) where customers require simultaneous delivery and pick-up service. in this paper, we propose an effective ant colony optimization (eaco) which includes insert, swap and 2-opt moves for solving vrpspd that is different with common an...

Fact Gathering means generating rule base from available numerical data or data base. The intelligence of a fuzzy system lies in its rule base. Generating rule base is one of the most important and difficult tasks when designing fuzzy systems. Various rule base generation methods are used such as Neural networks, genetic algorithms, biogeography based optimization approach, ant colony optimizat...

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...

this article presents the application of two algorithms: heuristic big bang-big crunch (hbb-bc) and a heuristic particle swarm ant colony optimization (hpsaco) to discrete optimization of reinforced concrete planar frames subject to combinations of gravity and lateral loads based on aci 318-08 code. the objective function is the total cost of the frame which includes the cost of concrete, formw...

This paper introduces one of the most profound Swarm Intelligence-based approaches, namely Ant Colony Optimization (ACO). The different operators in ACO and their effect on the quality of solution are briefly discussed. The ACO algorithms variants like Ant System (AS), Elitist Ant System (EAS), Rankbased Ant System (ASrank), Min-Max based Ant System (MMAS) are detailed. The paper puts forth a s...

nowadays, software cost estimation (sce) with machine learning techniques are more performance than other traditional techniques which were based on algorithmic techniques. in this paper, we present a new hybrid model of multi-layer perceptron (mlp) artificial neural network (ann) and ant colony optimization (aco) algorithm for high accuracy in sce called multilayer perceptron ant colony optimi...

— This paper examines ant colony optimization technique for edge detection. ACO is a technique to find out solutions for combinatorial optimization problem. The essential part of ACO algorithms is the combination of prior information about the structure of a solution with post information about the structure of previously obtained good solutions. Keywords— Ant Colony Optimization, Edge Detectio...

This survey focuses on the problem of parameters selection in image edge detection by ant colony optimization (ACO) algorithm. By introducing particle swarm optimization (PSO) algorithm to optimize parameters in ACO algorithm, the fitness function based on connectivity of image edge is proposed to evaluate the quality of parameters in ACO algorithm. And the ACO-PSO algorithm is applied to image...

Ant Colony Optimization (ACO) is a well-known metaheuristic in which a colony of artificial ants cooperates in exploring good solutions to a combinatorial optimization problem. In this paper, an ACO algorithm is presented for the graph coloring problem. This ACO algorithm conforms to Max-Min Ant System structure and exploits a local search heuristic to improve its performance. Experimental resu...

Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied toNP-hard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, that of executing parallel independent runs of an algorithm. The empirical tests are performed applyin...