Hazardous material transport accidents are events with low probability and high consequence risk. With the increase in proportion of hazardous materials transported on domestic roads, increasing number scholars have begun to study this field. In study, a multi-objective route planning model considering road traffic resilience carbon, which considers uncertainty demand time is under limit window...

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based...

Clustering problems appear in a wide range of unsupervised classification applications such as pattern recognition, vector quantization, data mining and knowledge discovery. The k-means algorithm is one of the most widely used clustering techniques. Unfortunately, k-means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is qui...

The protein side chain packing problem (PSCPP) is an essential issue for predicting structure in proteomics. PSCPP has been proved to be NP-hard. In this paper, we propose a method for solving PSCPP by transforming it to the graph clique problem, and then applying the ant colony optimization (ACO) algorithm to solve it. We build the coordinate rotamer library based on the pair of dihedral angle...

Ant Colony Optimization is an evolutionary method that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem. Compared to an existing ACO algorithm for the unweighted Total Tar-diness Problem our algorithm has several improvements. The main novelty is that in our algorithm the ants are guided on their way to good so...

the one-dimensional cutting stock problem, has so many applications in lots of industrial processes and during the past few years has attracted so many researchers’ attention all over the world. in this paper a meta-heuristic method based on aco is presented to solve this problem. in this algorithm, based on designed probabilistic laws, artificial ants do select various cuts and then select the...

Ant colony optimization (ACO) is a class of heuristic algorithms proposed to solve optimization problems. The idea was inspired by the behavior of real ants, related to their ability to find the shortest path between the nest and the food source. ACO has been applied successfully to different kinds of problems. So, this manuscript describes the development and application of an ACO algorithm to...

Abstract: One of the equipment that can help improve distribution system status today and reduce the cost of fault time is remote control switches (RCS). Finding the optimal location and number of these switches in the distribution system can be modeled with various objective functions as a nonlinear optimization problem to improve system reliability and cost. In this article, a particle swarm ...

We propose a novel adaptation of the Ant Colony Optimization (ACO) Technique for the resolution of analog sizing optimization problems. The paper details the proposed algorithm and highlights its performances using some mathematical test functions. This novel adaptation of the ACO technique is used for the optimal design of analog circuits, namely, a differential pair current conveyor and an in...

The problem of determining whether a set of tasks can be assigned to a set of heterogeneous processors in general is NP-hard. Generating an efficient schedule of tasks for a given application is critical for achieving high performance in a heterogeneous computing environment. This paper presents a novel algorithm based on Ant Colony Optimization (ACO) for the scheduling problem. An attempt is m...