DEACO: Adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem

Ebadinezhad, Sahar

doi:10.1016/j.engappai.2020.103649

Cited by 94 publications

(50 citation statements)

References 38 publications

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“…Figure 1 illustrates the process of the Dynamic Flying Ant Colony Optimization DFACO [11] algorithm which is modified from FACO and is added to the process of ACO. More detail and information about variation of the modified ACO algorithm can be found in our previous work [12].…”

Section: Variation Of Improved Aco Algorithm To Avoid the Problem Of mentioning

confidence: 99%

“…These proposed algorithms are inspired from DFACO [11] and the recent works based on ACO for Internet of Vehicles (IoVs) environment [32] and for Travel Salesman Problems (TSP) [12] which have proved their performance superiorities over many algorithms in the literature. These methods cluster the finest feasible paths that are discovered by ants.…”

Section: Architecture Of Cdfaco Cr-dfaco Cm-dfaco Cw-dfaco and Cmwmentioning

confidence: 99%

“…The cM-DFACO protocol applies two phases of modification on cDFACO as dynamic evaporation rate during pheromone update globally and replacing a new pheromone matrix. This novel technique is proposed by our previous study on IoV [32] and application of that on TSP [12]. Algorithm 2 illustrates the abstract of this scheme.…”

Section: Cm-dfaco Protocolmentioning

confidence: 99%

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Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Ebadinezhad

2021

SN Appl. Sci.

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This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

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Section: Variation Of Improved Aco Algorithm To Avoid the Problem Of mentioning

confidence: 99%

Section: Architecture Of Cdfaco Cr-dfaco Cm-dfaco Cw-dfaco and Cmwmentioning

confidence: 99%

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Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Ebadinezhad

2021

SN Appl. Sci.

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“…This algorithm takes time to solve large TSP problems. In [6], ant colony optimization (ACO) algorithm is used to solve the traveling salesman problem (TSP). The main challenge with this algorithm is that it is not exact and is very difficult to know how far the solution obtained is from the exact one.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a dummy guided formulation and exact solution method for TSP

Munapo

2020

EEJET

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Задача комівояжера (ЗК) -це задача, при якій комівояжер відправляється з вихідного вузла і повертається до нього таким чином, що кожен вузол в мережі вузлів відвідується один раз, а загальна пройдена відстаньзводиться до мінімуму. Вважається, що ефективного алгоритму для ЗК не існує. ЗК класифікується як NP-важка задача і розробка ефективного вирішення для неї буде означати NP=P. У статті представлена постановка задачі комівояжера з фіктивними елементами. Для цього виключаються всі субтури в мережі задачі комівояжера (ЗК) з використанням мінімально можливої кількості обмежень. Оскільки для формування субтура потрібно мінімум три вузли, мережа ЗК розділяється за допомогою вертикальних і горизонтальних ліній таким чином, щоб між вертикальними або горизонтальними лініями було не більше трьох вузлів. У даній роботі множина всіх вузлів між будь-якою парою вертикальних або горизонтальних ліній називається блоком. Фіктивні вузли використовуються для з'єднання одного блоку з наступним. Потім реконструйована ЗК використовується для постановки ЗК як задачі цілочислового лінійного програмування (ЦЛП). При використанні алгоритмів розгалуження немає ніякої гарантії, що кількість підзадач не злетить до некерованих рівнів. Евристичні або апроксимуючі алгоритми, які іноді використовуються для прийняття швидких рішень для практичних моделей ЗК, мають серйозні економічні проблеми. Різниця між точним рішенням і приблизними в грошовому вираженні дуже велика для практичних завдань, таких як доставка листів з використанням транспортного засобу в Пекіні, Токіо, Вашингтоні і т. д. Модель ЗК має безліч промислових застосувань, таких як свердління друкованих плат (ДП), капітальний ремонт газотурбінних двигунів, рентгенівська кристалографія, підключення комп'ютерів, комплектація замовлень на складах, складання маршрутів транспортних засобів, нанесення масок при виробництві ДП і т. д. Ключові слова: задача комівояжера, субтур, блок, цілочисельна лінійна програма, фіктивний/манекен

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“…The original PSO did not have strong exploration and exploitation, many scholars improved it and enhanced its optimization ability [12][13][14][15]. After continuous exploration by researchers, the swarm-based algorithm has developed rapidly, Ant colony algorithm is also a common swarm-based algorithm [16], which is often used to solve path planning problems [17][18] and traveling salesman problems [19][20]. Other classic algorithms include Artificial Fish Swarm Algorithm (AFSA) [21], Artificial bee colony algorithm(ABC) [22].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

et al. 2020

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Henry Gas Solubility Optimization Algorithm (HGSO) is a physical-based algorithm based on Henry's Law and simulates the process that the solubility of gas in liquid changes with temperature. The search strategy of the HGSO is very simple, which results in the algorithm having poor exploitation ability and unable to find a more accurate optimal solution. The Harris Hawk optimization algorithm (HHO) has strong exploitation because of its diverse exploitation strategies. In order to make up for the shortcomings of HGSO algorithm, this paper proposes an improved Henry gas solubility optimization algorithm (HHO-HGSO) based on the Harris Hawk optimization. During the iteration, taking the escaping energy function of the HHO algorithm as an indicator and determining a threshold, when the escaping energy function value is greater than the threshold, the algorithm conducts the search strategy of the HGSO algorithm, when less than the threshold, executes four exploitation strategies of Harris Hawk. In order to verify the performance of the proposed algorithm, HHO-HGSO is used to optimize CEC2005 and CEC2017 benchmark test functions and solve 4 real engineering design problems. Marine predators algorithm (MPA), Whale optimization algorithm (WOA), Lightning search algorithm (LSA), Water cycle algorithm (WCA), HGSO, and HHO were used to conduct comparative experiments. The simulation results show that the proposed HHO-HGSO algorithm has strong optimization ability. The core code of the paper has been uploaded in https://github.com/xwaiyy123/HHO-HGSO.

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DEACO: Adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem

Cited by 94 publications

References 38 publications

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Development of a dummy guided formulation and exact solution method for TSP

Hybrid Henry Gas Solubility Optimization Algorithm Based on the Harris Hawk Optimization

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