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Elevator group control scheduling is to dispatch every elevator to serve call requests from different floors based on some certain goal. It's a kind of typical combinatorial optimization problems. Ant colony algorithm is good at solving the discrete combinatorial optimization, its well global optimization ability and quick convergence velocity are both necessary to a scheduling algorithm. Moreover,...
Ant colony optimization(ACO) provides an effective way to solve combinatorial optimization problem. However, with the complexity of the problem increasing, the ACO algorithm needs considerable computational time and resources to improve the good quality of solution, and this rarely satisfies the requirement of real-time computing in M&S (Modeling and Simulation) area. Parallel implementation of...
Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for combinatorial optimization problems. This paper introduces a new type of ACO algorithm that will be used for routing along multiple routes in a network as opposed to optimizing a single route. Contrary to traditional routing algorithms, the Ant Dispersion Routing (ADR) algorithm has the objective of determining...
The following topics are dealt with: ant colony optimization; molecular and quantum computing; artificial life; particle swarm intelligence; bioinformatics and bioengineering; representation and operators; coevolution and collective behavior; artificial immune systems; combinatorial and numerical optimization; autonomous mental and behavior development; constraint and uncertainty handling; cognitive...
In this work, we first propose an NP-hard combinatorial problem, that is, the storage and retrieve (S/R) machine travel path optimization for batch order picking (BOP). Successful solving this problem is valuable to many application areas such inventory items in logistics and work-in-process storage in manufacturing systems. And then, we investigate the feasibility of using ant colony optimization...
Ant colony optimization (ACO) has become quite popular in recent years. It has been successfully applied to many combinatorial optimization problems. In contrast to many successful applications, the theoretical foundation of this randomized search heuristic is rather weak. One of problem is embodied in the termination condition of ACO. The few kinds of possible termination condition are used in experiment,...
For improved competitiveness, many modern firms have embraced the supply chain management to increase organizational effectiveness and achieve such organizational goals as improved customer value, better utilization of resources, and increased profitability. One main issue of logistics supply chain management is to find suitable optimized design mechanisms when a corporation decides to open and operate...
As a new general-purpose heuristic algorithm, Ant Colony Optimization Algorithm(ACO) has been applied to combinatorial optimization problems. The main characteristics of ACO are positive feedback, distributed computation and the use of constructive greedy heuristic. This paper presents the principle and the hierarchy model of the ACO with small-perturbation. This algorithm is applied to the optimization...
As a new model of intelligent computing, ant colony optimization (ACO) is a great success on combinatorial optimization problems, however, but research is relatively less in solving problems on continuous space optimization. Based on the mechanism and mathematical model of ant colony algorithm, mutation operation is introduced. The global and local updating rules of ant colony algorithm are improved...
The Least Significant Bit (LSB) Substitution is a kind of information hiding method. The secret message is embedded into the last r bits of a cover image to get away from the notice of hackers. The security and stego-image quality are two main issues of the LSB substitution method. Therefore, some researchers propose an LSB substitution matrix to address these two issues. Finding an optimal LSB substitution...
In this paper, we present an ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, which is also used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the...
Ant colony optimization (ACO) is a stochastic approach for solving combinatorial optimization problems like routing in computer networks. The idea of this optimization is based on the food accumulation methodology of the ant community. Zone based routing algorithms is build on the concept of individual node's position for routing of packets in mobile ad-hoc networks. Here the nodes' position can be...
This article modifies the evolutionary policy selection algorithm of Chang et al., which was designed for use in infinite horizon Markov decision processes (MDPs) with a large action space to a discrete stochastic optimization problem, in an algorithm called Evolutionary Policy Iteration-Monte Carlo (EPI-MC). EPI-MC allows EPI to be used in a stochastic combinatorial optimization setting with a finite...
This article established artificial neural networks based on improved ant colony optimization evaluation model for residential performance. Firstly, on the basis of comprehensive analysis of the effects factors of residential building's performance, considering of the advantages of dealing with non-linear object of neural network, the neural network is trained by the sample data. While training neural...
To predict protein structure based on Hydrophobic-Polar model (HP model) in two-dimensional space is called 2D HP protein folding problem. Ant Colony Optimization (ACO), which is inspired by the foraging behavior of ants, is a popular heuristic approach for solving combinatorial optimization problems. This paper presents a method of solving the 2D HP protein folding problem by parallel ACO algorithm...
In this paper an assembly line balance optimization model (ALBO) is proposed to solve the assembly line balance problem (ALB). It is a typical combinational optimization problem where pieces of work are transported between the work stations. In the ALB problem, the ultimate goal is to seek the optimal make span. ALB is currently dependent on human experts. We employed an evolutionary algorithm based...
Ant colony optimization (ACO) is a popular-based, artificial agent, general-search technique for the solution of difficult combinatorial problems. This paper presents a solution to the resource-constraint project scheduling problem based on ACO algorithm. The method considers the quantified duration and resource as the heuristic information to calculate the accurate state transition probability and...
In this paper, ant colony optimization (ACO) algorithm is introduced to redundant instruments placement for optimum process variable estimation accuracy. It is proved that additional redundancy measurement will enhance estimation accuracy if the measurements relate the process variables in a different way, whereas the quantity of accuracy improvement is determined by the measurements structure. To...
Ant colony optimization (ACO) algorithm is usually utilized to solve various combinatorial optimization problems. In this work, however, two novel ant systems are developed to estimate the state of interest, and we call them ant estimators. The first ant estimator is based partly upon the idea of particle filter, while the latter depends on the movement of each ant. For each ant estimator, the ldquopheromonerdquo...
In this paper, a general framework for solving combinatorial optimization problems heuristically by the ant system approach is developed. Based on the two different conditions, some convergence properties for ant colony system (ACS) are presented. The global searching and convergence ability are improved by adaptively changing the lower pheromone bound. It is shown that ACS is guaranteed to find an...
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