Dynamic impact for ant colony optimization algorithm

Skackauskas, Jonas; Kalganova, Tatiana; Dear, Ian; Janakiram, Mani

doi:10.1016/j.swevo.2021.100993

Cited by 28 publications

(9 citation statements)

References 92 publications

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“…All three dynamic optimization strategies are implemented within the same baseline ACO core algorithm, solving static MKP benchmarks [58]. High-quality MKP solutions were possible to achieve by utilizing a dynamic impact evaluation method in the ACO edge's probability calculation.…”

Section: Baseline Aco Algorithm and Optimization Systemmentioning

confidence: 99%

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Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems

Skackauskas¹,

Kalganova

2023

Preprint

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Section: Baseline Aco Algorithm and Optimization Systemmentioning

confidence: 99%

“…ACO algorithm parameters have been tuned in previous research [58] and used throughout all experimentation. The best combination of pheromone parameters are: 𝜏 𝑚𝑎𝑥 = 1, 𝜏 𝑚𝑖𝑛 = 0.001, 𝜏 0 = 1, ∆𝜏 0 = 1, 𝜌 = 0.1.…”

Section: Baseline Aco Algorithm and Optimization Systemmentioning

confidence: 99%

Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems

Skackauskas¹,

Kalganova

2023

Preprint

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“…where τ i,j shows the number of edge pheromones i, j, and α defines the effect of the pheromone, η i,j specifies the edge desirability. i, j, and β define influence of desirability [35].…”

Section: Creation Phasementioning

confidence: 99%

Ergonomic Risk Minimization in the Portuguese Wine Industry: A Task Scheduling Optimization Method Based on the Ant Colony Optimization Algorithm

2022

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In the wine industry, task planning is based on decision-making processes that are influenced by technical and organizational constraints as well as regulatory limitations. A characteristic constraint inherent to this sector concerns occupational risks, in which companies must reduce and mitigate work-related accidents, resulting in lower operating costs and a gain in human, financial, and material efficiency. This work proposes a task scheduling optimization model using a methodology based on the ant colony optimization approach to mitigate the ergonomic risks identified in general winery production processes by estimating the metabolic energy expenditure during the execution of tasks. The results show that the tasks were reorganized according to their degree of ergonomic risk, preserving an acceptable priority sequence of tasks with operational affinity and satisfactory efficiency from the point of view of the operationalization of processes, while the potential ergonomic risks are simultaneously minimized by the rotation and alternation of operative teams between those tasks with higher and lower values of metabolic energy required. We also verified that tasks with lower ergonomic-load requirements influence the reorganization of the task sequence by lowering the overall value of metabolic energy, which is reflected in the reduction of the ergonomic load.

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“…First, comparison was performed on simple WEISH instances, where most algorithms in the literature can achieve optimum solution, therefore performance is measured in terms of the success rate (how many times algorithm was able to achieve optimum) or in terms of the average error percentage error (2) across all instances. For the comparison, the six best performing algorithms were selected from the literature, which include Ant Colony Optimization with Dynamic impact (ACOwD) described in [11], Improved Whale Optimization Algorithm (IWOA) [22], two variations of binary differential search TE-BDS and TR-BDS proposed in [43], and two implementations of Particle Swarm Optimization (PSO) with self-adaptive check and repair -SACRO-CBPSOTVAC and SACRO-BPSOTVAC [19]. Results in Fig.…”

Section: Comparisons To the State-of-the-artmentioning

confidence: 99%

“…Comprehensive literature review of solving MKPs was provided by [8] and a more recent MKP overview by [9] summarizes algorithms used for solving MKP. This paper focuses on the state-of-the-art population and metaheuristic algorithms used for solving MKP instances, such as ant colony optimization ( [10], [11]), various types of genetic algorithms ( [12], [13], [14]), evolutionary algorithms ( [15], [16], [17]), variations of particle swarm optimization algorithm ( [18], [19]), binary harmony search [20], binary cuckoo search algorithm [21], whale optimization algorithm [22] and etc. Most of the research in population-based algorithms focuses on small MKP instances with 𝑛 ≤ 100, while only few explore large instances with 𝑛 = 500 and above.…”

Section: Introductionmentioning

confidence: 99%

Imperialist Competitive Algorithm with Independence and Constrained Assimilation

Dzalbs

Kalganova

Dear

2020

2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

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This work proposes an improved Imperialist Competitive Algorithm (ICA) based algorithm for solving constrained combinatorial problems, called ICA with Independence and Constrained Assimilation (ICAwICA). The proposed algorithm introduces the concept of colony independence -a free will to choose between classic ICA assimilation to the empire's imperialist or any other imperialist in the population. Furthermore, a constrained assimilation process has been implemented that combines classical ICA assimilation and revolution operators, while maintaining population diversity. In order to evaluate the performance and generalisation aspects of the proposed approach, two different kinds of combinatorial benchmark problems were selectedsubset selection and routing, Multiple Knapsack Problem (MKP) and Multiple Depot Vehicle Routing Problem (MDVRP), respectively. The algorithm showed definite improvement over classic ICA and outperformed most of the competition on both types of problems across multiple instances, indicating the generic, universal nature of the ICAwICA. Moreover, it ranked 2 nd among the recently published algorithms that are customised to the specific problem with the use of problem-specific operators, while the proposed algorithm had no such operators.

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Dynamic impact for ant colony optimization algorithm

Cited by 28 publications

References 92 publications

Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems

Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems

Ergonomic Risk Minimization in the Portuguese Wine Industry: A Task Scheduling Optimization Method Based on the Ant Colony Optimization Algorithm

Imperialist Competitive Algorithm with Independence and Constrained Assimilation

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