This paper proposes an extension method for Ant Colony Optimization (ACO) algorithm called Dynamic Impact. Impact is designed to improve convergence and solution quality solving challenging optimization problems that have a non-linear relationship between resource consumption fitness. proposed tested against the real-world Microchip Manufacturing Plant Production Floor (MMPPFO) problem theoreti...

In this paper we propose an ant colony optimization variant where several independent colonies try to simultaneously solve the same problem. The approach includes a migration mechanism that ensures the exchange of information between colonies and a mutation operator that aims to adjust the parameter settings during the optimization. The proposed method was applied to several benchmark instances...

Solving large traveling salesman problem (TSP) in an efficient way is a challenging area for the researchers of computer science. This paper presents a modified version of the ant colony system (ACS) algorithm called Red-Black Ant Colony System (RB-ACS) for the solutions of TSP which is the most prominent member of the combinatorial optimization problem. RB-ACS uses the concept of ant colony sy...

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of...

Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This paper extends ant colony systems and discusses a novel data...

A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.

A method of automatic programming, called genetic programming, assumes that the desired program is found by using a genetic algorithm. We propose an idea of ant colony programming in which instead of a genetic algorithm an ant colony algorithm is applied to search for the program. The test results demonstrate that the proposed idea can be used with success to solve the approximation problems.

the traveling salesman problem (tsp) is a well-known combinatorial optimization problem and holds a central place in logistics management. the tsp has received much attention because of its practical applications in industrial problems. many exact, heuristic and metaheuristic approaches have been proposed to solve tsp in recent years. in this paper, a modified ant colony optimization (maco) is ...

One merit of genetic algorithm is fast overall searching, but this algorithm usually results in low efficiency because of large quantities of redundant codes. The advantages of ant colony algorithm are strong suitability and good robustness while its disadvantages are tendency to stagnation, slow speed of convergence. Put forward based on improved ant colony algorithm for wireless sensor networ...