Ant Colony Optimization for Optimal Low-Pass State Variable Filter Sizing

Loubna, Kritele; Benhala, Bachir; Zorkani, Izeddine

doi:10.11591/ijece.v8i1.pp227-235

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…The other whales change their positions toward to the best answer. Based on Mirjalili and all, we have the (2) to (5):…”

Section: Encircling Preymentioning

confidence: 99%

“…The problem of determining the travel route is similar to finding the solution from the TSP problem [3]. Since TSP is an NP-hard problem and it takes a protracted time to search out a tour among the cities, the complexity order of this problem becomes exponential which does not have a suitable execution time [4], but an intelligent method requires less computation time and more accuracy [5]. Metaheuristic algorithms are powerful methods for solving many tough optimization problems [6].…”

Section: Introductionmentioning

confidence: 99%

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Solving a traveling salesman problem using meta-heuristics

Nejad

Fazekas

2022

IJ-AI

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In this article, we have introduced an advanced new method of solving a traveling salesman problem (TSP) with the whale optimization algorithm (WOA), and K-means which is a partitioning-based algorithm used in clustering. The whale optimization algorithm first was introduced in 2016 and later used to solve a TSP problem. In the TSP problem, finding the best path, which is the path with the lowest value in the fitness function, has always been difficult and time-consuming. In our algorithm, we want to find the best tour by combining it with K-means which is a clustering method. In other words, we want to divide our problem into smaller parts called clusters, and then we join the clusters based on their distances. To do this, the WOA algorithm, TSP, and K-means must be combined. Separately, the WOA-TSP algorithm which is an unclustered algorithm is also implemented to be compared with the proposed algorithm. The results are shown through some figures and tables, which prove the effectiveness of this new method.

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“…The other whales change their positions toward to the best answer. Based on Mirjalili and all, we have the (2) to (5):…”

Section: Encircling Preymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solving a traveling salesman problem using meta-heuristics

Nejad

Fazekas

2022

IJ-AI

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“…Trial vector Ui is compared to target vector Xi in the selection operation. For next-generation, the individual with the best fitness will be selected as (7). The pseudocode of the DE is as shown in algorithm 2.…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…Hence, these problems can be formulated as optimization problems, which optimization algorithms can fix. Metaheuristic methods have been suggested in the literature to tackle challenging problems in diverse domains [2], such as genetic algorithm (GA) [3], differential evolution (DE) [4], [5], particle swarm optimization (PSO) [6], ant colony optimization (ACO) [7]- [9], and artificial bee colony (ABC) [10]- [12]. Nevertheless, many researchers use hybrid metaheuristic methods to overcome many optimization issues.…”

Section: Introductionmentioning

confidence: 99%

An optimal design of current conveyors using a hybrid-based metaheuristic algorithm

Abi

Benhala

2022

IJECE

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<span lang="EN-US">This paper focuses on the optimal sizing of a positive second-generation current conveyor (CCII+), employing a hybrid algorithm named DE-ACO, which is derived from the combination of differential evolution (DE) and ant colony optimization (ACO) algorithms. The basic idea of this hybridization is to apply the DE algorithm for the ACO algorithm’s initialization stage. Benchmark test functions were used to evaluate the proposed algorithm’s performance regarding the quality of the optimal solution, robustness, and computation time. Furthermore, the DE-ACO has been applied to optimize the CCII+ performances. SPICE simulation is utilized to validate the achieved results, and a comparison with the standard DE and ACO algorithms is reported. The results highlight that DE-ACO outperforms both ACO and DE.</span>

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“…In addition, Loubna et al [17] conducted research on the optimization of ant colony for measuring low-pass state variable filters by drawing conclusions from the ant colony optimization technique for the optimal size of the state variable filter. The SPICE simulation confirms the validity of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

et al. 2021

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The ant colony algorithm is an algorithm adopted from the behavior of ants which naturally ants are able to find the shortest route on the way from the nest to places of food sources based on footprints on the track that has been passed. The ant colony algorithm helps a lot in solving several problems such as scheduling, traveling salesman problems (TSP) and vehicle routing problems (VRP). In addition, ant colony has been developed and has several variants. However, in its function to find the shortest distance is optimized by utilizing several normalized distance formulas with the data used in finding distances between merchants in the mercant ecosystem. Where in the test normalized distance is superior Hamming distance in finding the shortest distance of 0.2875, then followed by the same value, namely the normalized formula Manhattan distance and normalized Euclidean distance with a value of 0.4675 and without using the normalized distance formula or the original ant colony algorithm gets a value 0.6635. Given the sensitivity in distance search using merchant ecosystem data, the method works well on the ant colony Algorithm using normalized Hamming distance.

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Ant Colony Optimization for Optimal Low-Pass State Variable Filter Sizing

Cited by 9 publications

References 12 publications

Solving a traveling salesman problem using meta-heuristics

Solving a traveling salesman problem using meta-heuristics

An optimal design of current conveyors using a hybrid-based metaheuristic algorithm

Sensitivity of shortest distance search in the ant colony algorithm with varying normalized distance formulas

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