This paper sets up an Artificial Bee Colony (ABC) algorithm for evolving ant direction Ant Colony Optimization (ACO) to solve the Economic Power Dispatch (EPD) problem. The method ABC-ACO employs the bee colony to find a suitable and the best values operators to improve the ACO. The feasibility of the proposed approach was tested on IEEE 57-bus system. The proposed approach simulation results, ...

we propose a novel hybrid algorithm named ACO-FA, which integrates the merits of ant colony optimization (ACO) with firefly algorithm (FA) to solve unconstrained optimization problems. The main feature of the hybrid algorithm is to hybridize the solution construction mechanism of the ACO with the FA. In our hybrid algorithm, the initial solutions are generated randomly from the search space, an...

AIM To investigate change in the area of anterior capsular opening (ACO) after cataract surgery and its relation to the degree of postoperative anterior inflammation in patients with diabetes mellitus (DM). METHODS 31 eyes of 31 patients with DM and 30 eyes of 30 normal controls scheduled to undergo cataract surgery were examined prospectively. The area of ACO was measured with an anterior ey...

Aconitine (ACO) is well-known for causing lethal ventricular tachyarrhythmias. While cardiac Na+ channel opening during repolarization has long been documented in animal cardiac myocytes, the cellular effects and mechanism of ACO in human remain unexplored. This study aimed to assess the proarrhythmic effects of ACO in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). ACO ...

In this paper we describe the application of a Simple ACO (S-ACO) as a method of optimization for membership functions’ parameters of a fuzzy logic controller (FLC) in order to find the optimal intelligent controller for an Autonomous Wheeled Mobile Robot. Simulation results show that ACO outperforms a GA in the optimization of FLCs for an autonomous mobile robot.

Most real world engineering design problems, such as cross-country water mains, include combinations of continuous, discrete, and binary value decision variables. Very often, the binary decision variables associate with the presence and/or absence of some nominated alternatives or project’s components. This study extends an existing continuous Ant Colony Optimization (ACO) algorithm to simultan...

Ant colony optimization (ACO) is a metaheuristic that produces good results for a wide range of combinatorial optimization problems. Often such successful applications use a combination of ACO and local search procedures that improve the solutions constructed by the ants. In this paper, we study this combination from a theoretical point of view and point out situations where introducing local s...

Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving one...

We introduce two reactive frameworks for dynamic adapating some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters: pheromone trails are associated with parameter values; these pheromone trails represent the learnt desirability of using parameter values and are used to dynamically set parameters in a probabilistic way. The two framewor...