Ant Colony Optimization (ACO) is a meta-heuristic iterative algorithm used to solve different combinatorial optimization problems. In this method, a number of artificial ants build solutions for an optimization problem and exchange information on their quality through a communication scheme that is similar to the one adopted by real ants. In this paper, Ant Colony Optimization is used to solve ...

Nature is the immense gifted source for solving complex problems. It always helps to find the optimal solution to solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide research area of networks which has set of independent nodes. The characteristics involved in MANET’s are Dynamic, does not depend on any fixed infrastructure or centralized networks, High mobility. The Bio-Inspired algorith...

This paper presents a solution to the global optimization of continuous functions by the Differential Ant-Stigmergy Algorithm (DASA). The DASA is a newly developed algorithm for continuous optimization problems, utilizing the stigmergic behavior of the artificial ant colonies. It is applied to the high-dimensional real-parameter optimization with low number of function evaluations. The performa...

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...

Total weighted tardiness is a measure of customer satisfaction. Minimizing it represents satisfying the general requirement of on-time delivery. In this research, we consider an ant colony optimization (ACO) algorithm to solve the problem of scheduling unrelated parallel machines to minimize total weighted tardiness. The problem is NP-hard in the strong sense. Computational results show that th...

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative ...

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...