Comparing the performances of six nature-inspired algorithms on a real-world discrete optimization problem

Haklı, Hüseyin; Uğuz, Harun; Ortacay, Zeynep

doi:10.1007/s00500-022-07466-1

Cited by 2 publications

(2 citation statements)

References 54 publications

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“…We use a metaheuristic population-based evolutionary optimization tool called a genetic algorithm (GA). It outperforms other nature-inspired algorithms on real-world discrete optimization problems, primarily in faster convergences and other performance indicators (along with the scatter search algorithm) [28].…”

Section: Introductionmentioning

confidence: 98%

When Fairness Meets Consistency in AHP Pairwise Comparisons

Dodevska¹,

Radovanović

Petrović

et al. 2023

Mathematics

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We propose introducing fairness constraints to one of the most famous multi-criteria decision-making methods, the analytic hierarchy process (AHP). We offer a solution that guarantees consistency while respecting legally binding fairness constraints in AHP pairwise comparison matrices. Through a synthetic experiment, we generate the comparison matrices of different sizes and ranges/levels of the initial parameters (i.e., consistency ratio and disparate impact). We optimize disparate impact for various combinations of these initial parameters and observed matrix sizes while respecting an acceptable level of consistency and minimizing deviations of pairwise comparison matrices (or their upper triangles) before and after the optimization. We use a metaheuristic genetic algorithm to set the dually motivating problem and operate a discrete optimization procedure (in connection with Saaty’s 9-point scale). The results confirm the initial hypothesis (with 99.5% validity concerning 2800 optimization runs) that achieving fair ranking while respecting consistency in AHP pairwise comparison matrices (when comparing alternatives regarding given criterium) is possible, thus meeting two challenging goals simultaneously. This research contributes to the initiatives directed toward unbiased decision-making, either automated or algorithm-assisted (which is the case covered by this research).

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Section: Introductionmentioning

confidence: 98%

When Fairness Meets Consistency in AHP Pairwise Comparisons

Dodevska¹,

Radovanović

Petrović

et al. 2023

Mathematics

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“…The extent to which these two basic criteria are fulfilled has a direct impact on the success of the land reallocation process. However, except for a few studies (Haber et al, 2022; Hakli et al, 2022; Haklı et al, 2018), reports of these two basic criteria are rare in the literature.…”

Section: Introductionmentioning

confidence: 99%

A novel preference‐based artificial‐bee‐colony algorithm approach to the land reallocation optimization problem in a land consolidation case study: DOT village in Turkey

Özbeyaz

İnceyol

2023

Expert Systems

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Land consolidation (LC) is a widely used spatial land management tool. The most essential stage of the LC process is land reallocation, which comprises the exchange of property rights. For this reason, land reallocation may be a potential source of dissatisfaction among farmers, and thus, optimization techniques are continually being developed to minimize these dissatisfactions. The goal of this study was to develop a novel preference‐based artificial‐bee‐colony (ABC) algorithm for use in the land reallocation project for DOT village. In the study, the farmer preferences were prioritized. Specifically, 70% of the lands were preference‐based, with the remainder being random. Furthermore, the ABC algorithm's performance was assessed using three limit parameter (LP) values and two probability functions. The best success was attained when the LP was set to 50 and the roulette‐wheel probability function was employed. The success of land reallocation was defined by the occupancy rate in the blocks and the reallocation rates based on the preferences of the farmer's lands. The occupancy rate in all the blocks using the ABC algorithm was 95.54%, the full occupancy rate of blocks according to farmer preferences was 38%, and the land reallocation rate based on the farmers' preferences was 73%. The remaining lands were distributed to the block areas at random using the ABC method. Moreover, the success of the preference‐based population surpassed that of randomly formed populations.

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Comparing the performances of six nature-inspired algorithms on a real-world discrete optimization problem

Cited by 2 publications

References 54 publications

When Fairness Meets Consistency in AHP Pairwise Comparisons

When Fairness Meets Consistency in AHP Pairwise Comparisons

A novel preference‐based artificial‐bee‐colony algorithm approach to the land reallocation optimization problem in a land consolidation case study: DOT village in Turkey

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