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Related Experiment Video

Updated: May 12, 2026

Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

D2MOPSO: MOPSO based on decomposition and dominance with archiving using crowding distance in objective and solution

N Al Moubayed¹, A Petrovski, J McCall

¹Robert Gordon University, Aberdeen, AB25 1HG, UK n.al-moubayed@rgu.ac.uk.

Evolutionary Computation

|April 26, 2013

Summary

This study enhances a multi-objective particle swarm optimizer (D2MOPSO) using dominance and decomposition for better diversity and coverage. The improved algorithm demonstrates high competitiveness and efficiency across various multi-objective optimization problems.

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Area of Science:

Computational Intelligence
Optimization Algorithms
Evolutionary Computation

Background:

Multi-objective optimization problems (MOPs) present challenges in balancing competing objectives.
Decomposition simplifies MOPs by breaking them into smaller aggregation problems.
Dominance is crucial for managing archives in multi-objective optimization.

Purpose of the Study:

To improve the D2MOPSO algorithm by refining its archiving technique.
To enhance diversity and coverage in both objective and solution spaces.
To evaluate the improved D2MOPSO's performance on standard MOP benchmarks.

Main Methods:

The study improves the D2MOPSO algorithm, integrating dominance and decomposition.
A novel archiving technique is introduced to enhance solution quality and distribution.
Performance is assessed using constrained and unconstrained benchmark test problems.

Main Results:

The improved D2MOPSO shows superior or competitive performance compared to MOEA/D, OMOPSO, and dMOPSO.
Statistical tests confirm the algorithm's effectiveness and efficiency.
The method achieves better diversity and coverage in objective and solution spaces.

Conclusions:

The enhanced D2MOPSO algorithm is highly competitive and efficient for multi-objective optimization.
The proposed improvements lead to better performance on a wide range of MOPs.
The algorithm is applicable to both constrained and unconstrained optimization tasks.