High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

Pan, Han; You, Xiaoming; Liu, Sheng

doi:10.1109/access.2020.3019445

Cited by 6 publications

(4 citation statements)

References 62 publications

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“…It is noteworthy that the error rates for instances att532 and att48 are substantial for all methods. The best-known solutions for both cases are substantially higher, with 86,729 and 33,522 route lengths, respectively, according to recent research [54][55][56]. Therefore, if we adopted these values as the real optimal solutions, the error rates would be relatively small, 5.3% and 3.9% for att532 and att48, respectively.…”

Section: Resultsmentioning

confidence: 92%

An Efficiency Boost for Genetic Algorithms: Initializing the GA with the Iterative Approximate Method for Optimizing the Traveling Salesman Problem—Experimental Insights

Alkafaween,

Hassanat,

Essa

et al. 2024

Applied Sciences

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The genetic algorithm (GA) is a well-known metaheuristic approach for dealing with complex problems with a wide search space. In genetic algorithms (GAs), the quality of individuals in the initial population is important in determining the final optimal solution. The classic GA using the random population seeding technique is effective and straightforward, but the generated population may contain individuals with low fitness, delaying convergence to the best solution. On the other side, heuristic population seeding strategies provide the advantages of producing individuals with high fitness and encouraging rapid convergence to the optimal solution. Using background information on the problem being solved, researchers have developed several population seeding approaches. In this paper, to enhance the genetic algorithm efficiency, we propose a new method for the initial population seeding based on a greedy approach. The proposed method starts by adding four extreme cities to the route, creating a loop, and then adding each city to the route through a greedy strategy that calculates the cost of adding every city to different locations along the route. This method identifies the best position to place the city as well as the best city to add. Employing local constant permutations improves the resultant route even more. Together with the suggested approach, we examine the GA’s effectiveness while employing conventional population seeding methods like nearest-neighbor, regression-based, and random seeding. Utilizing some of the well-known Traveling Salesman Problem (TSP) examples from the TSPLIB, the standard library for TSPs, tests were conducted. In terms of the error rate, average convergence, and time, the experimental results demonstrate that the GA that employs the suggested population seeding technique performs better than other GAs that use conventional population seeding strategies.

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Section: Resultsmentioning

confidence: 92%

An Efficiency Boost for Genetic Algorithms: Initializing the GA with the Iterative Approximate Method for Optimizing the Traveling Salesman Problem—Experimental Insights

Alkafaween,

Hassanat,

Essa

et al. 2024

Applied Sciences

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“…In order to avoid stagnation, the pheromone is limited between [τ min , τ max ], and the upper and lower bounds of the pheromone are determined by the calculation method in the literature [41]. In addition, the initial pheromone is set at τ max (0) to improve the global search ability of ants in the initial period.…”

Section: Pheromone Updatementioning

confidence: 99%

Solution to Solid Wood Board Cutting Stock Problem

et al. 2021

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In the production process for wooden furniture, the raw material costs account for more than 50% of furniture costs, and the utilization rate of raw materials depends mainly on the layout scheme. Therefore, a reasonable layout is an important measure to reduce furniture costs. This paper investigates the solid wood board cutting stock problem (CSP) and establishes an optimization model, with the goal of the highest possible utilization rate for original boards. An ant colony-immune genetic algorithm (ACIGA) is designed to solve this model. The solutions of the ant colony algorithm are used as the initial population of the immune genetic algorithm, and the optimal solution is obtained using the immune genetic algorithm after multiple iterations are transformed into the accumulation of global pheromones, which improves the search ability and ensures the solution quality. The layout process of the solid wood board is abstracted into the construction process of the solution. At the same time, in order to prevent premature convergence, several improved methods, such as a global pheromone hybrid update and adaptive crossover probability, are proposed. Comparative experiments are designed to verify the feasibility and effectiveness of the ACIGA, and the experimental results show that the ACIGA has better solution precision and global search ability compared with the ant colony algorithm (ACA), genetic algorithm (GA), grey wolf optimizer (GWO), and polar bear optimization (PBO). The utilization rate increased by more than 2.308%, which provides effective theoretical and methodological support for furniture enterprises to improve economic benefits.

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“…where, 𝑃 𝑏𝑒𝑠𝑡 is the probability of finding the optimal solution when the MMAS algorithm converges, which is generally 0.05 [33].…”

Section: 24mentioning

confidence: 99%

“…Apart from the 𝑃 𝑏𝑒𝑠𝑡 and Q parameters whose values were respectively adjusted to 0.05 and 0.9 by considering the results produced in literature [33,34] and the verification tests carried out, the other parameters are adjusted here. The parameters are adjusted by sampling and interpreting several measurements.…”

Section: Parameters Settingmentioning

confidence: 99%

A Novel Hybrid Algorithm of Max-Min Ant System with Quadratic Programming to Solve the Unit Commitment Problem

Akoue¹,

Eloundou²,

Essiane³

et al. 2021

JESA

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In this paper, we propose a novel hybrid algorithm based on MAX-MIN Ant System version of ant colony optimization coupled with quadratic programming (MMAS-QP). Quadratic programming is used to optimize the Economic Dispatching process and MMAS for planning the switching schedule of a set of production units. The algorithm is implemented in MATLAB software environment for two systems, one is 4 generating units running for 8 hours, and the other is 10 generating units running for 24 hours. The impact of heuristic parameters on the behavior of the algorithm is highlighted through the parameters setting. Results obtained shows improved solution compared to several methods such as Modified Ant Colony Optimization (MACO), particle Swarm Optimization combined with Lagrange Relaxation (PSO-LR), Swarm and Evolutionary Computation (SEC), Particle Swarm Optimization combined with Genetic Algorithm (PSO-GA). The proposed method improves sufficiently the quality of the solution as well as the execution time.

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High-Frequency Path Mining-Based Reward and Punishment Mechanism for Multi-Colony Ant Colony Optimization

Cited by 6 publications

References 62 publications

An Efficiency Boost for Genetic Algorithms: Initializing the GA with the Iterative Approximate Method for Optimizing the Traveling Salesman Problem—Experimental Insights

An Efficiency Boost for Genetic Algorithms: Initializing the GA with the Iterative Approximate Method for Optimizing the Traveling Salesman Problem—Experimental Insights

Solution to Solid Wood Board Cutting Stock Problem

A Novel Hybrid Algorithm of Max-Min Ant System with Quadratic Programming to Solve the Unit Commitment Problem

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