Artificial Bee and Ant Colony Optimization

Lindfield, George; Penny, J.E.T.

doi:10.1016/b978-0-12-803636-5.00007-4

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…Metaheuristic algorithms, such as the genetic algorithm [18][19][20], annealing algorithm [21], tabu search algorithm [22], particle swarm optimization algorithm [23], and ant colony algorithm [24][25][26], have gained popularity in recent years for solving complex problems that cannot be solved by traditional methods [27][28][29]. Consequently, many researchers have utilized these metaheuristic global optimization algorithms to address multi-objective path planning.…”

Section: Related Workmentioning

confidence: 99%

“…The ant colony selects the path by sensing the concentration of pheromones on the path. Under the update mechanism, a better path for foraging is finally found [24]. After practice and development, the grid-based ant colony path planning is applied in this paper, and the path planning for crossing obstacles can quickly converge and obtain a better solution.…”

Section: Grid-based Ant Colony Algorithmmentioning

confidence: 99%

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Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Zhao,

Zhang,

Shao

et al. 2023

Electronics

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Path planning is crucial in the scheduling and motion planning of multiple robots. However, solving multi-robot path-planning problems efficiently and quickly is challenging due to their high complexity and long computational time, especially when dealing with many robots. This paper presents a unified mathematical model and algorithm for the path planning of multiple robots moving from one formation to another in an area with obstacles. The problem was initially simplified by constructing a cost matrix, and then the route planning was achieved by integrating an elite enhanced multi-population genetic algorithm and an ant colony algorithm. The performance of the proposed planning method was verified through numerical simulations in various scenarios. The findings indicate that this method exhibits high computational efficiency and yields a minimal overall path distance when addressing the path-planning problem of a multi-robot formation reconstruction. As a result, it holds promising potential for the path-planning problem of a multi-robot formation reconstruction.

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Section: Related Workmentioning

confidence: 99%

Section: Grid-based Ant Colony Algorithmmentioning

confidence: 99%

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Zhao,

Zhang,

Shao

et al. 2023

Electronics

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“…After each worker bee completes its search cycle, it exchanges this information with specialist bees. In a real beehive, this is done by a bee dance, but this formal dance can convey the direction, distance and quality of the nectar source to the viewer (Lindfeid and Penny, 2017, p. 120). The position of a food source, Xi = [xi1, xi2,…, xiD], indicates a possible solution, and the amount of nectar of a food source corresponds to the percentage of the associated solution.…”

Section: Artificial Bee Colony Algorithm (Abc)mentioning

confidence: 99%

Portfolio optimization in the capital market bubble space, an application of meta-heuristic algorithms

Mohammadi

Khoshouei²,

Chadegani³

2022

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PurposeIn this study, to maximize returns and minimize investment risk, an attempt was made to form an optimal portfolio under conditions where the capital market has a price bubble. According to the purpose, the research was of the applied type, in terms of data, quantitative and postevent, and in terms of the type of analysis, it was of the descriptive-correlation type. Sequence, skewness and kurtosis tests were used to identify the months with bubbles from 2015 to 2021 in the Tehran Stock Exchange. After identifying the bubble courses, artificial bee colony meta-heuristic and invasive weed algorithms were used to optimize the portfolio. The purpose of this paper is to address these issues.Design/methodology/approachThe existence of bubbles in the market, especially in the capital market, can prevent the participation of investors in the capital market process and the correct allocation of financial resources for the economic development of the country. However, due to the goal of investors to achieve a portfolio of high returns with the least amount of risk, there is need to pay attention to these markets increases.FindingsThe results identify 14 periods of price bubbles during the study period. Additionally, stock portfolios with maximum returns and minimum risk were selected for portfolio optimization. According to the results of using meta-heuristic algorithms to optimize the portfolio, in relation to the obtained returns and risk, no significant difference was observed between the returns and risk of periods with price bubbles in each of the two meta-heuristic algorithms. This study can guide investors in identifying bubble courses and forming an optimal portfolio under these conditions.Research limitations/implicationsOne of the limitations of this research is the non-generalizability of the findings to stock exchanges of other countries and other time periods due to the condition of the price bubble, as well as other companies in the stock market due to the restrictions considered for selecting the statistical sample.Originality/valueThis study intends to form an optimal stock portfolio in a situation where the capital market suffers from a price bubble. This study provides an effective and practical solution for investors in the field of stock portfolio optimization.

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Artificial Bee and Ant Colony Optimization

Cited by 2 publications

References 15 publications

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

Portfolio optimization in the capital market bubble space, an application of meta-heuristic algorithms

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