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Titlebook: Handbook of Nature-Inspired Optimization Algorithms: The State of the Art; Volume I: Solving Si Ali Mohamed,Diego Oliva,Ponnuthurai Nagarat

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11#
發(fā)表于 2025-3-23 12:01:44 | 只看該作者
,Beetle Antennae Search Algorithm for?the?Motion Planning of?Industrial Manipulator,vel but directly uses forward kinematics to construct antennae fitness function. The experiment is practiced in the CoppeliaSim simulator for the industrial IIWA Kuka industrial manipulator to verify the performance.
12#
發(fā)表于 2025-3-23 15:00:37 | 只看該作者
13#
發(fā)表于 2025-3-23 20:03:05 | 只看該作者
14#
發(fā)表于 2025-3-24 01:38:34 | 只看該作者
15#
發(fā)表于 2025-3-24 04:32:00 | 只看該作者
,Beetle Antennae Search Algorithm for?the?Motion Planning of?Industrial Manipulator,d engineering applications. In this chapter, BAS is applied to the redundancy resolution of an industrial manipulator with dynamic joint velocity constraints by searching in high-dimensional space. The addressed application does not need to construct inverse kinematics equations in joint velocity le
16#
發(fā)表于 2025-3-24 06:56:18 | 只看該作者
17#
發(fā)表于 2025-3-24 10:53:43 | 只看該作者
Parameter Estimation of Per-Unit Photovoltaic Models Using Optimization Algorithms: Comparative Stum renewable sources is essential to meet climate goals without slowing down economic growth and reducing well-being. Particularly, cost reduction in photovoltaic production technologies allows increases in installed capacity, contributing to a less expensive energy transition. Therefore, using tools
18#
發(fā)表于 2025-3-24 16:06:20 | 只看該作者
,Space–Time Concept in Social Network Search Algorithm,luence on each other, and similarity in their behavior are the main features of users in the social networks. These features are utilized in the development of an optimization method, which is called the Social Network Search (SNS) algorithm. The SNS method is a recently developed optimizer, which m
19#
發(fā)表于 2025-3-24 20:31:19 | 只看該作者
20#
發(fā)表于 2025-3-25 03:14:33 | 只看該作者
,Antenna Array Design Using Differential Evolution with?Ranking-Based Mutation Operators,is book chapter. We apply a design framework based on Differential Evolution (DE) with ranking-based mutation operators. This DE variant uses a different mutation operator than the original DE. The basic idea in this DE variant is to probabilistically select the vectors for the mutation operator bas
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