Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Lagrange Multipliers: Two Constraints

Lagrange Multipliers: Two Constraints

The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Vector Functions and Motion: Problem Solving

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

Lagrange Multipliers: Problem Solving

Lagrange Multipliers: Problem Solving

A silo with a cylindrical base, flat bottom, and hemispherical roof is a common design in agricultural and industrial storage due to its structural efficiency and ease of construction. Optimizing its dimensions to maximize storage capacity for a given amount of material—i.e., a fixed surface area—is a classic problem in applied calculus and engineering design. The key parameters are the radius r of the base and the height h of the cylindrical section.The total volume of the silo is obtained by...

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

Optimization Problems

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Synergistically Enhanced Ta<sub>2</sub>O<sub>5</sub>/AgNPs SERS Substrate Coupled with Deep Learning for Ultra-Sensitive Microplastic Detection.

Materials (Basel, Switzerland)·2026

Same author

Preparation and SERS applications of Ta<sub>2</sub>O<sub>5</sub> composite nanostructures.

Optics express·2023

Same author

Stringent starvation protein A and LuxI/LuxR-type quorum sensing system constitute a mutual positive regulation loop in Pseudoalteromonas.

Biochemical and biophysical research communications·2020

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 12, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Multi-objective gold rush optimization algorithm: Theoretical Extensions and applications in UAV path planning.

Kecheng Su¹, Yaoyang Wang², Yikang Kong³

¹School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.

|June 10, 2026

Summary

This summary is machine-generated.

This study introduces the Multi-Objective Gold Rush Optimization algorithm (MOGRO) for complex engineering problems. MOGRO enhances UAV path planning by optimizing path length and obstacle avoidance, showing superior performance.

Related Experiment Videos

Last Updated: Jun 12, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Engineering and Computer Science
Artificial Intelligence and Optimization

Background:

Multi-objective optimization problems are crucial in engineering and science.
UAV path planning is a significant application area demanding efficient solutions.

Purpose of the Study:

To propose an innovative multi-objective extension of the Gold Rush Optimization algorithm (GRO), named MOGRO.
To address the challenge of obtaining Pareto-optimal solutions in multi-objective optimization.
To apply MOGRO to UAV path planning considering path length and obstacle threats.

Main Methods:

Developed the Multi-Objective Gold Rush Optimization algorithm (MOGRO).
Incorporated a reference-point-guided two-level selection mechanism and an external archive strategy.
Validated MOGRO against seven advanced algorithms on 18 benchmark problems and a UAV path planning model.

Main Results:

MOGRO demonstrated significantly superior performance compared to existing algorithms.
The algorithm showed improvements in convergence, distribution, and solution quality.
MOGRO provided an effective solution for multi-objective UAV path planning.

Conclusions:

MOGRO offers an innovative and effective approach to multi-objective optimization.
The algorithm provides a valuable tool for complex UAV path planning scenarios.
This research contributes to the theoretical framework of GRO and practical applications in robotics.