Preface to “Swarm and Evolutionary Computation—Bridging Theory and Practice”

Kim, Yong-Hyuk; Caraffini, Fabio

doi:10.3390/math11051209

Cited by 1 publication

(1 citation statement)

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“…Our study presents two assignment strategies: one minimizing work hours based on South Korea’s regulations and another minimizing skimmer usage. To determine these assignments, we employ a genetic algorithm (GA) [ 4 , 5 ], which has been representative among swarm and evolutionary computation methods [ 6 ], and compare the results with current practices in South Korea through simulations. However, the complexity and time-intensiveness of simulation-based evaluations necessitate alternative approaches.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Approach to Optimal Oil Skimmer Assignment for Oil Spill Response: A Case Study

Kim,

Cho

et al. 2024

Biomimetics

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We propose a genetic algorithm for optimizing oil skimmer assignments, introducing a tailored repair operation for constrained assignments. Methods essentially involve simulation-based evaluation to ensure adherence to South Korea’s regulations. Results show that the optimized assignments, compared to current ones, reduced work time on average and led to a significant reduction in total skimmer capacity. Additionally, we present a deep neural network-based surrogate model, greatly enhancing efficiency compared to simulation-based optimization. Addressing inefficiencies in mobilizing locations that store oil skimmers, further optimization aimed to minimize mobilized locations and was validated through scenario-based simulations resembling actual situations. Based on major oil spills in South Korea, this strategy significantly reduced work time and required locations. These findings demonstrate the effectiveness of the proposed genetic algorithm and mobilized location minimization strategy in enhancing oil spill response operations.

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Section: Introductionmentioning

confidence: 99%