A Comparative Study of a Hybrid Ant Colony Algorithm MMACS for the Strongly Correlated Knapsack Problem

0-1 knapsack problem is a typical combinatorial optimization question in the design and analysis of algorithms. The mathematical description of the knapsack problem is given in theory. The 0-1 knapsack problem is solved by ant colony optimistic algorithm that is improved by introducing genetic operators. To solve the 0-1 knapsack problem with the improved ant colony algorithm, experimental results of numerical simulations, compared with greedy algorithm and dynamic programming algorithm, have shown obvious advantages in efficiency and accuracy on the knapsack problem.

Download Full-text

Comparative study between elephant herding optimization (EHO) and U-turning ant colony optimization (U-TACO) in solving symmetric traveling salesman problem (STSP)

Journal of Advanced Computer Science & Technology ◽

10.14419/jacst.v8i2.29403 ◽

2019 ◽

Vol 8 (2) ◽

pp. 32 ◽

Cited By ~ 1

Author(s):

Saman M. Almufti ◽

Ridwan Boya Marqas ◽

Renas R. Asaad

Keyword(s):

Comparative Study ◽

Ant Colony Optimization ◽

Traveling Salesman Problem ◽

Ant Colony Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Traveling Salesman ◽

Ant Colony ◽

Initial State ◽

Symmetric Traveling Salesman Problem

Swarm Intelligence is an active area of researches and one of the most well-known high-level techniques intended to generat, select or find a heuristic that optimize solutions of optimization problems.Elephant Herding optimization algorithm (EHO) is a metaheuristic swarm based search algorithm, which is used to solve various optimi-zation problems. The algorithm is deducted from the behavior of elephant groups in the wild. Were elephants live in a clan with a leader matriarch, while the male elephants separate from the group when they reach adulthood. This is used in the algorithm in two parts. First, the clan updating mechanism. Second, the separation mechanism.U-Turning Ant colony optimization (U-TACO) is a swarm-based algorithm uses the behavior of real ant in finding the shortest way be-tween its current location and a source of food for solving optimization problems. U-Turning Ant colony Optimization based on making partial tour as an initial state for the basic Ant Colony algorithm (ACO).In this paper, a Comparative study has been done between the previous mentioned algorithms (EHO, U-TACO) in solving Symmetric Traveling Salesman Problem (STSP) which is one of the most well-known NP-Hard problems in the optimization field. The paper pro-vides tables for the results obtained by EHO and U-TACO for various STSP problems from the TSPLIB95.

Download Full-text

A Modified Ant Colony Algorithm to the P| Prec| Cmax Scheduling Problem

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2013070105 ◽

2013 ◽

Vol 4 (3) ◽

pp. 65-74

Author(s):

Mohamed Messaoudi-Ouchene ◽

Ali Derbala

Keyword(s):

Comparative Study ◽

Ant Colony Algorithm ◽

Ant Colony ◽

Ant Colony System ◽

Scheduling Problem ◽

Priority Rules ◽

Dynamic Information ◽

Taboo Search ◽

Longest Path ◽

The Comparative Study

This paper investigates a comparative study which addresses the P/prec/Cmax scheduling problem, a notable NP-hard benchmark. MLP_SACS, a modified ant colony algorithm, is used to solve it. Its application provides us a better job allocation to machines. In front of each machine, the jobs are performed with three priority rules, the longest path (LP), a modified longest path (MLP) and a maximum between two values (MAX). With these three rules and with both static and dynamic information heuristics called “visibility”, six versions of this ant colony algorithm are obtained, studied and compared. The comparative study analyzes the following four meta-heuristics, simulated annealing, taboo search, genetic algorithm and MLP_SACS (a modified ant colony system), is performed. The solutions obtained by the MLP_SACS algorithm are shown to be the best.

Download Full-text

MapReduce-Based Ant Colony Optimization Algorithm for Multi-Dimensional Knapsack Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.1877 ◽

2013 ◽

Vol 380-384 ◽

pp. 1877-1880 ◽

Cited By ~ 3

Author(s):

Rui Tao Liu ◽

Xiu Jian Lv

Keyword(s):

Ant Colony Optimization ◽

Knapsack Problem ◽

Large Scale ◽

Ant Colony Algorithm ◽

Search Time ◽

Ant Colony ◽

Ant Colony Optimization Algorithm ◽

Processing Power ◽

Crossover And Mutation ◽

Computing Simulation

This paper uses MapReduce parallel programming mode to make the Ant Colony Optimization (ACO) algorithm parallel and bring forward the MapReduce-based improved ACO for Multi-dimensional Knapsack Problem (MKP). A variety of techniques, such as change the probability calculation of the timing, roulette, crossover and mutation, are applied for improving the drawback of the ACO and complexity of the algorithm is greatly reduced. It is applied to distributed parallel as to solve the large-scale MKP in cloud computing. Simulation experimental results show that the algorithm can improve the defects of long search time for ant colony algorithm and the processing power for large-scale problems.

Download Full-text