Titlebook: Bio-Inspired Computing: Theories and Applications; 17th International C Linqiang Pan,Dongming Zhao,Jianqing Lin Conference proceedings 2023

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Exposition

Research on Multi-modal Multi-objective Path Planning by Improved Ant Colonyblem. However, the existing algorithms to solve the path problem can only find a single optimal path, cannot satisfactorily find multiple groups of optimal solutions at the same time, and it is very necessary to propose as many solutions as possible. So this paper carries out a research on the Multi

無法解釋

Local Path Planning Algorithm Designed for Unmanned Surface Vessel Based on Improved Genetic Algoritod and genetic obstacle is above the globally planned algorithm. Among them, genetic algorithm has strong spatial search ability and strong adaptive ability. However, due to the low efficiency of the traditional genetic algorithm, it cannot meet the needs of the real-time path planning of unmanned s

HERE

HUMP

拍翅

S-Plane Controller Parameter Tuning Based on IAFSA for UUVing error caused by manually setting S-plane control parameters, the artificial fish swarm algorithm is improved by adopting methods such as predatory behavior, adaptive step size, and field of view with attenuation factor to improve the optimization performance of the artificial fish swarm. The imp

Hemoptysis

A Reinforcement-Learning-Driven Bees Algorithm for?Large-Scale Earth Observation Satellite Schedulinuild a mathematical programming model of the EOSSP. After that, we propose a reinforcement-learning-driven bees algorithm (RLBA) to solve a large-scale EOSSP (LSEOSSP). The RLBA adopts a Q-learning method to select search operations from global search and neighbourhood search. We define a new state

Euphonious

頭盔

Global Path Planning for Unmanned Ships Based on Improved Particle Swarm Algorithmt navigation environment. To address the problem that the particle swarm algorithm is easy to fall into local optimum at the later stage, we first integrate chaos theory into the basic particle swarm algorithm, and generate chaotic population and replace some particles that fall into local optimum b

Hallmark

A Self-adaptive Single-Objective Multitasking Optimization AlgorithmTOs often perform better than conventional single-task evolutionary. Transferring knowledge plays a very important role in multitask optimization algorithms. Many existing methods transfer elite solutions between tasks to improve algorithm performance, however, these methods may or produce negative