An Improved Equilibrium Optimizer for Solving Multi-quay Berth Allocation Problem

International Journal of Computational Intelligence Systems, Год журнала: 2024, Номер 17(1)

Опубликована: Июль 3, 2024

Abstract The multi-quay berth allocation problem (MQBAP) is an important in the planning of seaside operations (POSO) to find best berthing solution for all vessels. In this paper, efficient method based on equilibrium optimizer (EO) proposed MQBAP. dynamic multi-swarm strategy (DMS) improve rapid decline population diversity during iterative process EO, which subsequently applied a certain improvement also made original model MQBAP by proposing alternate quay selection mechanism, aims make more complete. To verify effectiveness algorithm MQBAP, paper uses six test cases and seven comparative algorithms it comprehensively from total service cost, time, location. results show that DEO achieved smallest costs 7584 19,889 medium-scale, 44,998, 38,899, 57,626 large-scale systems.

Язык: Английский

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Bernstein-based oppositional-multiple learning and differential enhanced exponential distribution optimizer for real-world optimization problems

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 138, С. 109370 - 109370

Опубликована: Окт. 23, 2024

Язык: Английский

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Topological search and gradient descent boosted Runge–Kutta optimiser with application to engineering design and feature selection

CAAI Transactions on Intelligence Technology, Год журнала: 2024, Номер unknown

Опубликована: Окт. 24, 2024

Abstract The Runge–Kutta optimiser (RUN) algorithm, renowned for its powerful optimisation capabilities, faces challenges in dealing with increasing complexity real‐world problems. Specifically, it shows deficiencies terms of limited local exploration capabilities and less precise solutions. Therefore, this research aims to integrate the topological search (TS) mechanism gradient rule (GSR) into framework RUN, introducing an enhanced algorithm called TGRUN improve performance original algorithm. TS employs a circular scheme conduct thorough solution regions surrounding each solution, enabling careful examination valuable areas enhancing algorithm’s effectiveness exploration. To prevent from becoming trapped optima, GSR also integrates descent principles direct wider investigation global space. This study conducted serious experiments on IEEE CEC2017 comprehensive benchmark function assess TGRUN. Additionally, evaluation includes engineering design feature selection problems serving as additional test assessing validation outcomes indicate significant improvement accuracy

Язык: Английский

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Q-learning assisted multi-objective evolutionary optimization for low-carbon scheduling of open-pit mine trucks

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 92, С. 101778 - 101778

Опубликована: Дек. 3, 2024

Язык: Английский

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Reinforcement learning-driven dual neighborhood structure artificial bee colony algorithm for continuous optimization problem

Applied Soft Computing, Год журнала: 2024, Номер unknown, С. 112601 - 112601

Опубликована: Дек. 1, 2024

Язык: Английский

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