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...

in this paper, the problem of layout optimization for x-bracing of steel frames is studied using the ant system (as). a new design method is employed to share the gravity and the lateral loads between the main frame and the bracings according to the requirements of the ibc2006 code. an algorithm is developed which is called optimum steel designer (osd). an optimization method based on an approx...

Wireless Body Area Networks (WBANs) are emerging in the livestock industry for remote monitoring of cattle using wireless body sensors (WBS). The random mobility animals acting as nodes causes network’s topology to change rapidly, originating from scalability and reliability issues. Stable transmission acquired data base station requires an intelligent clustering mechanism that reduces energy c...

Ant colony optimization and artificial potential field were used respectively as global path planning and local path planning methods in this paper. Some modifications were made to accommodate ant colony optimization to path planning. Pheromone generated by ant colony optimization was also utilized to prevent artificial potential field from getting local minimum. Simulation results showed that ...

Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to exi...

Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to exi...

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 paper presents a modification of the ant colony optimization algorithm (ACO) intended to introduce psychology factor of emotion into the algorithm. We define only two emotions ants could have, positive and negative, and correspond to two reaction to perception respectively. For avoiding premature convergence allows Emotional Ant Colony Optimization (EACO) to continue search for better even...

Instance reduction is a pre-processing step devised to improve the task of classification. algorithms search for reduced set instances mitigate low computational efficiency and high storage requirements. Hence, finding optimal subset utmost importance. Metaheuristic techniques are used as potential application. Antlion optimization (ALO) recent metaheuristic algorithm that simulates antlion’s f...