In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) an...

Scientific workflows comprising of many computational tasks including data dependency may require multiple and heterogeneous amount of computing resources during runtime. Scheduling such workflows with the objective of achieving minimal makespan and cost and maximal resource usage is a challenge in any computing environment. The researchers aim at developing novel algorithm to schedule scientif...

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...

Two Ant Colony Optimization algorithms are proposed to tackle multiobjective structural optimization problems with an additional constraint. A cardinality constraint is introduced in order to limit the number of distinct values of the design variables appearing in any candidate solution. Such constraint is directly enforced when an ant builds a candidate solution, while the other mechanical con...

Quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in many fields. Many real world applications such as backboard wiring, typewriter keyboard design and scheduling can be formulated as QAPs. Ant colony algorithm is a multi-agent system inspired by behaviors of real ant colonies to solve optimization problems. Ant colony optimization (ACO) is one of new ...

-In this article two different optimization algorithms are presented to solve the deficiency of ant colony algorithm such as slow convergence rate and easy to fall into local optimum. This method based on Max-Min Ant System, established an adaptive model for pheromone evaporation coefficient adjusted adaptively and avoided the ants falling into local optimum. At the same time, this optimization...

To enhance the optimization ability of ant colony optimization, this paper proposes a Bloch sphere-based quantum-inspired ant colony optimization algorithm. In the proposed approach, the positions of ants are encoded by qubits described on Bloch sphere. First, the destinations of ants are obtained by the select probability designed by the pheromone and heuristic information, and then, the movem...

The traveling salesman problem (TSP) is one of the most important combinational optimization problems that have nowadays received much attention because of its practical applications in industrial and service problems. In this paper, a hybrid two-phase meta-heuristic algorithm called MACSGA used for solving the TSP is presented. At the first stage, the TSP is solved by the modified ant colony s...

Ant colony optimization is an ant algorithm framework that took inspiration from foraging behavior of ant colonies. Indeed, ACO algorithms use a chemical communication, represented by pheromone trails, to build good solutions. However, ants involve different communication channels to interact. Thus, this paper introduces the acoustic communication between ants while they are foraging. This proc...

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...