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

Jinge Shi,

Yi Chen, Ali Asghar Heidari

и другие.

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

Multi-threshold image segmentation using new strategies enhanced whale optimization for lupus nephritis pathological images DOI
Jinge Shi, Yi Chen, Chaofan Wang

и другие.

Displays, Год журнала: 2024, Номер 84, С. 102799 - 102799

Опубликована: Июль 20, 2024

Язык: Английский

Процитировано

1

A decomposition-based many-objective evolutionary algorithm with Q-learning guide weight vectors update DOI

H. Zhang,

Yiru Dai

Expert Systems with Applications, Год журнала: 2024, Номер 262, С. 125607 - 125607

Опубликована: Ноя. 7, 2024

Язык: Английский

Процитировано

1

Reinforcement learning-driven dual neighborhood structure artificial bee colony algorithm for continuous optimization problem DOI
Tingyu Ye, Fang Li, Hui Wang

и другие.

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

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1

An Improved Equilibrium Optimizer for Solving Multi-quay Berth Allocation Problem DOI Creative Commons
Qifang Luo, Panpan Song, Yongquan Zhou

и другие.

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

Процитировано

0

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

Jinge Shi,

Yi Chen, Ali Asghar Heidari

и другие.

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

Процитировано

0