Improving Search Efficiency Adopting Hill-Climbing to Ant Colony Optimization for Constraint Satisfaction Problems

2011 ◽  
Author(s):  
Daiki Hayakawa ◽  
Kazunori Mizuno ◽  
Hitoshi Sasaki ◽  
Seiichi Nishihara
Keyword(s):  
Ant Colony Optimization ◽  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Ant Colony ◽  
Hill Climbing ◽  
Search Efficiency

Population Based Techniques for Solving the Student Project Allocation Problem

2020 ◽  
Vol 11 (2) ◽  
pp. 192-207 ◽  
Author(s):  
Patrick Kenekayoro ◽  
Promise Mebine ◽  
Bodouowei Godswill Zipamone
Keyword(s):  
Ant Colony Optimization ◽  
Constraint Satisfaction ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Based ◽  
Constraint Satisfaction Problems ◽  
Allocation Problem ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Gravitational Search

The student project allocation problem is a well-known constraint satisfaction problem that involves assigning students to projects or supervisors based on a number of criteria. This study investigates the use of population-based strategies inspired from physical phenomena (gravitational search algorithm), evolutionary strategies (genetic algorithm), and swarm intelligence (ant colony optimization) to solve the Student Project Allocation problem for a case study from a real university. A population of solutions to the Student Project Allocation problem is represented as lists of integers, and the individuals in the population share information through population-based heuristics to find more optimal solutions. All three techniques produced satisfactory results and the adapted gravitational search algorithm for discrete variables will be useful for other constraint satisfaction problems. However, the ant colony optimization algorithm outperformed the genetic and gravitational search algorithms for finding optimal solutions to the student project allocation problem in this study.

scholarly journals An Ant Colony Optimization Based on Information Entropy for Constraint Satisfaction Problems

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 766 ◽  
Author(s):  
Boxin Guan ◽  
Yuhai Zhao ◽  
Yuan Li
Keyword(s):  
Local Search ◽  
Ant Colony Optimization ◽  
Constraint Satisfaction ◽  
Information Entropy ◽  
Hypothesis Test ◽  
Constraint Satisfaction Problem ◽  
Constraint Satisfaction Problems ◽  
Ant Colony ◽  
Cost Comparison ◽  
Solution Quality

Solving the constraint satisfaction problem (CSP) is to find an assignment of values to variables that satisfies a set of constraints. Ant colony optimization (ACO) is an efficient algorithm for solving CSPs. However, the existing ACO-based algorithms suffer from the constructed assignment with high cost. To improve the solution quality of ACO for solving CSPs, an ant colony optimization based on information entropy (ACOE) is proposed in this paper. The proposed algorithm can automatically call a crossover-based local search according to real-time information entropy. We first describe ACOE for solving CSPs and show how it constructs assignments. Then, we use a ranking-based strategy to update the pheromone, which weights the pheromone according to the rank of these ants. Furthermore, we introduce the crossover-based local search that uses a crossover operation to optimize the current best assignment. Finally, we compare ACOE with seven algorithms on binary CSPs. The experimental results revealed that our method outperformed the other compared algorithms in terms of the cost comparison, data distribution, convergence performance, and hypothesis test.

scholarly journals Improved Ant Algorithms for Software Testing Cases Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Shunkun Yang ◽  
Tianlong Man ◽  
Jiaqi Xu
Keyword(s):  
Ant Colony Optimization ◽  
Software Testing ◽  
Feedback Mechanism ◽  
Ant Colony ◽  
Search Model ◽  
Search Efficiency ◽  
Improved Method ◽  
Random Algorithm ◽  
Positive Feedback Mechanism ◽  
Update Strategy

Existing ant colony optimization (ACO) for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO), and improved the global path pheromone update strategy for ant colony optimization (IGPACO). At last, we put forward a comprehensive improved ant colony optimization (ACIACO), which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND) and genetic algorithm (GA) in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.

scholarly journals A HYBRID ANT COLONY OPTIMIZATION ALGORITHM FOR SOLVING A HIGHLY CONSTRAINED NURSE ROSTERING PROBLEM

2019 ◽  
Author(s):  
Razamin Ramli ◽  
Rosshairy Abd Rahman ◽  
Nurdalila Rohim
Keyword(s):  
Ant Colony Optimization ◽  
Hybrid Model ◽  
Ant Colony ◽  
Fine Tuning ◽  
Hill Climbing ◽  
Nurse Rostering ◽  
Ant Colony Optimization Algorithm ◽  
Nurse Rostering Problem ◽  
Balanced Distribution ◽  
The Hill

Distribution of work shifts and off days to nurses in a duty roster is a crucial task. In hospital wards, much effort is spent trying to produce workable and quality rosters for their nurses. However, there are cases, such as mandatory working days per week and balanced distribution of shift types that could not be achieved in the manually generated rosters, which are still being practiced. Hence, this study focused on solving those issues arising in nurse rostering problems (NRPs) strategizing on a hybrid of Ant Colony Optimization (ACO) algorithm with a hill climbing technique. The hybridization with the hill climbing is aiming at fine-tuning the initial solution or roster generated by the ACO algorithm to achieve better rosters. The hybrid model is developed with the goal of satisfying the hard constraints, while minimizing the violation of soft constraints in such a way that fulfill hospital’s rules and nurses’ preferences. The real data used for this highly constrained NRPs was obtained from a large Malaysian hospital. Specifically, three main phases were involved in developing the hybrid model, which are generating an initial roster, updating the roster through the ACO algorithm, and implementing the hill climbing to further search for a refined solution. The results show that at a larger value of pheromone, the chance of obtaining a good solution was found with only small penalty values. This study has proven that the hybrid ACO is able to solve NRPs with good potential solutions that fulfilled all the four important criteria, which are coverage, quality, flexibility, and cost. Subsequently, the hybridmodel is also beneficial to the hospital’s management whereby nurses can be scheduled with balanced distribution of shifts, which fulfil their preferences as well.  

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