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Updated: May 21, 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

Monkey jumping optimization: a tree-branch-inspired metaheuristic for global search.

Idriss Dagal¹, Yaya Dagal Dari²

¹Department of Electrical Engineering, Beykent University, Ayazağa Mahallesi, Hadım Koruyolu Cd. No:19, Sarıyer, İstanbul, Turkey.

Scientific Reports

|May 19, 2026

Summary

Monkey Jumping Optimization (MJO) is a new nature-inspired algorithm for complex problems. It shows faster convergence and higher success rates than other methods, offering a flexible framework for optimization tasks.

Keywords:

Global optimization Metaheuristic algorithm Monkey jumping optimization Nature-inspired computing Unmanned aerial vehicle path planning

Related Experiment Videos

Last Updated: May 21, 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:

Computational Intelligence
Optimization Algorithms
Nature-Inspired Computing

Background:

Complex global optimization problems require efficient algorithms.
Existing metaheuristics face challenges in balancing exploration and exploitation.
Nature-inspired algorithms offer novel approaches to problem-solving.

Purpose of the Study:

Introduce the Monkey Jumping Optimization (MJO) algorithm.
Evaluate MJO's performance against state-of-the-art optimization techniques.
Analyze the theoretical convergence properties of MJO.

Main Methods:

Developed MJO based on monkey locomotion, incorporating energy-aware leap dynamics, probabilistic branch selection, and canopy memory.
Benchmarked MJO against eight algorithms (PSO, GA, GWO, etc.) on standard test suites (CEC 2024).
Analyzed MJO's convergence using a Markov chain framework.

Main Results:

MJO demonstrated competitive performance, achieving up to 28.7% faster convergence than PSO on deceptive functions.
MJO attained 15-22% higher success rates in finding global optima across the CEC 2024 benchmark suite.
Theoretical analysis confirmed MJO's convergence properties.

Conclusions:

MJO is an effective nature-inspired algorithm for complex global optimization.
Its biologically inspired design and computational efficiency are suitable for engineering applications like UAV path planning and neural architecture search.
Further refinement is possible, particularly regarding parameter sensitivity and performance on unimodal problems.