scholarly journals Improved ant colony optimization for quantum cost reduction

2020 ◽  
Vol 9 (4) ◽  
pp. 1525-1532
Author(s):  
Shaveta Thakral ◽  
Dipali Bansal
Keyword(s):  
Ant Colony Optimization ◽  
Cost Reduction ◽  
Significant Contribution ◽  
Heuristic Algorithms ◽  
Reversible Logic ◽  
Ant Colony ◽  
Quantum Cost ◽  
Logic Function ◽  
Memory Space ◽  
Post Synthesis

Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved Ant Colony Optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead. 

A Novel ALU Circuit based on Reversible Logic

2019 ◽  
Vol 29 (11) ◽  
pp. 2050172
Author(s):  
Arindam Banerjee ◽  
Debesh Kumar Das
Keyword(s):  
Cost Reduction ◽  
Delay Line ◽  
Reversible Logic ◽  
Quantum Cost ◽  
Logic Function ◽  
Fabrication Cost

We propose a new ALU circuit based on reversible logic. The ALU circuit implements two addition methodologies. The outputs are generated at some fixed lines for each arithmetic or logic function. A satisfactory tradeoff is achieved between the line count and the quantum cost. Reduction in ancillary inputs and garbage outputs causes a decrease in fabrication cost. The proposed designs outperform the earlier designs with respect to delay, line count and number of operations. The libraries NOT–CNOT–V–[Formula: see text] are used to optimize the quantum cost of the proposed designs.

Ant Colony Optimization for Factory Layout

2012 ◽  
Vol 591-593 ◽  
pp. 758-761
Author(s):  
Xiu Zeng ◽  
Qian Li Ma
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Mean Value ◽  
Ant Colony ◽  
Logistics Cost ◽  
The Mean ◽  
Factory Layout ◽  
Np Problem

Factory layout is NP problem[1]. There are many methods to solve it ,such as engineering diagram, flow chart method, various heuristic algorithms, SA( simulated annealing) and GA(genetic algorithm) [2].ACO (ant colony optimization) is used to solve it in this paper. The logistics costs exist between two workshops that are treated as pheromone that guides ants to search the best solution. Smaller logistics cost is, stronger the two workshops of relation is. In the process of optimization theworkshop with low logistics cost is more likely to be chosen, which minimizes the system logistics cost. Compared with GA, ACO has the advantage in speed. The mean value of the solution, the best solution, the worst solution is better too. More the number of workshop is, more obvious the superiority is.

Intelligent Ant Colony System for Traveling Salesman Problem and Clustering

2011 ◽  
pp. 18-42
Author(s):  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Data Clustering ◽  
Heuristic Algorithms ◽  
Computation Time ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
K Nearest Neighbor ◽  
Ant Colony Systems

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 work parallelizes the ant colony systems and introduces the communication strategies so as to reduce the computation time and reach the better solution for traveling salesman problem. We also extend ant colony systems and discuss a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the ant colony optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters

Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

2015 ◽  
Vol 14 (10) ◽  
pp. 6176-6183
Author(s):  
S.J. Mohana ◽  
Dr.M. Saroja ◽  
Dr.M. Venkatachalam
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Heuristic Algorithms ◽  
Ant Colony ◽  
Workflow Scheduling ◽  
Ant Colony Optimization Algorithm ◽  
Computing Environment ◽  
Shared Resources ◽  
Cloud Computing Environment ◽  
Computing Model

Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

scholarly journals Dynamic Load Balancing Ant Colony Optimization (DLBACO) Algorithm for Task Scheduling in Cloud Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 939-946
Keyword(s):  
Load Balancing ◽  
Ant Colony Optimization ◽  
Task Scheduling ◽  
Dynamic Load ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Ant Colony ◽  
Dynamic Load Balancing ◽  
Cloud Environment ◽  
Task Scheduling Algorithm

Cloud computing is a framework which provides on-demand services to the user for scalability, security, and reliability based on pay as used service anytime & anywhere. For load balancing, task scheduling is the most critical issues in the cloud environment. There are so many meta-heuristic algorithms used to solve the load balancing problem. A good task scheduling algorithm should be used for optimum load balancing in cloud environment. Such scheduling algorithm must have some vital characteristic like minimum makespan, maximum throughput, and maximum resource utilization, etc. In this paper, a dynamic load balancing and task scheduling algorithm based on ant colony optimization (DLBACO) has been proposed. This algorithm assigns the task the VM which has highest probability of availability in minimum time. The proposed algorithm balances the whole system by minimizing the makespan of the task and maximizing the throughput. CloudSim simulator is used to simulate the proposed scheduling algorithm and results show that the proposed (DLBACO) algorithm is better than the existing algorithms such as FCFS, LBACO (Load balancing ACO), and primary ACO

scholarly journals Differential-Evolution-Based Coevolution Ant Colony Optimization Algorithm for Bayesian Network Structure Learning

Algorithms ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 188 ◽  
Author(s):  
Xiangyin Zhang ◽  
Yuying Xue ◽  
Xingyang Lu ◽  
Songmin Jia
Keyword(s):  
Differential Evolution ◽  
Ant Colony Optimization ◽  
Structure Learning ◽  
Heuristic Algorithms ◽  
Evolutionary Process ◽  
Ant Colony ◽  
Training Data ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Optimal Network

Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy.

scholarly journals The Hybrid ColorAnt-RT Algorithms and an Application to Register Allocation

2015 ◽  
Vol 18 (55) ◽  
pp. 81
Author(s):  
Mauro Mulati, ◽  
Carla Lintzmayer ◽  
Anderson Da Silva
Keyword(s):  
Ant Colony Optimization ◽  
Graph Coloring ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Register Allocation ◽  
Ant Colony ◽  
Frequency Assignment ◽  
It Use ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Optimization Algorithms

Ant Colony Optimization is a metaheuristic used to create heuristic algorithms to find good solutions for combinatorial optimization problems. This metaheuristic is inspired on the effective behavior present in some species of ants of exploring the environment to find and transport food to the nest. Several works have proposed using Ant Colony Optimization algorithms to solve problems such as vehicle routing, frequency assignment, scheduling and graph coloring. The graph coloring problem essentially consists in finding a number k of colors to assign to the vertices of a graph, so that there are no two adjacent vertices with the same color. This paper presents the hybrid ColorAnt-RT algorithms, a class of algorithms for graph coloring problems which is based on the Ant Colony Optimization metaheuristic and uses Tabu Search as local search. The experiments with ColorAnt-RT algorithms indicate that changing the way to reinforce the pheromone trail results in better results. In fact, the results with ColorAnt-RT show that it is a promising option in finding good approximations of k. The good results obtained by ColorAnt-RT motivated it use on a register allocation based on Ant Colony Optimization, called CARTRA. As a result, this paper also presents CARTRA, an algorithm that extends a classic graph coloring register allocator to use the graph coloring algorithm ColorAnt-RT. CARTRA minimizes the amount of spills, thereby improving the quality of the generated code.

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