Parameter learning using ant colony optimization for minimal time cost reduction

2015 ◽  
Author(s):  
Ji Dong ◽  
Huan Yang ◽  
Zhi-Heng Zhang ◽  
Fan Min
Keyword(s):  
Ant Colony Optimization ◽  
Cost Reduction ◽  
Ant Colony ◽  
Parameter Learning ◽  
Time Cost ◽  
Minimal Time

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. 

scholarly journals Meta-Heuristic Algorithm based on Ant Colony Optimization Algorithm and Project Scheduling Problem (PSP) for the Traveling Salesman Problem

2021 ◽  
Vol 15 ◽  
pp. 68-77
Author(s):  
Alejandro Fuentes-Penna ◽  
Jorge A. Ruiz-Vanoye ◽  
Marcos S. González-Ramírez
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Time Cost ◽  
Ant Colony Optimization Algorithm ◽  
Main Target ◽  
Project Scheduling Problem ◽  
Time Distance ◽  
The Traveling Salesman Problem

The main target of Traveling Salesman Problem (TSP) is to construct the path with the lowest time between different cities, visiting every one once. The Scheduling Project Ant Colony Optimization (SPANCO) Algorithm proposes a way to solve TSP problems adding three aspects: time, cost effort and scope, where the scope is the number of cities, the effort is calculated multiplying time, distance and delivering weight factors and dividing by the sum of them and optimizing the best way to visit the cities graph.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University
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