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Borg: an auto-adaptive many-objective evolutionary computing framework.

David Hadka¹, Patrick Reed

¹Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA. dmh309@psu.edu

Evolutionary Computation

|March 6, 2012

Summary

This summary is machine-generated.

The Borg multi-objective evolutionary algorithm (MOEA) offers a novel framework for complex optimization problems. It combines several techniques to outperform existing methods in many-objective, multimodal scenarios.

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Area of Science:

Optimization Algorithms
Computational Intelligence
Evolutionary Computation

Background:

Many-objective and multimodal optimization problems present significant computational challenges.
Existing multi-objective evolutionary algorithms (MOEAs) often struggle with the complexity of these problems.

Purpose of the Study:

Introduce the Borg multi-objective evolutionary algorithm (MOEA) as a unified framework for many-objective, multimodal optimization.
Evaluate the performance of the Borg MOEA against state-of-the-art algorithms.

Main Methods:

The Borg MOEA integrates ε-dominance, ε-progress, randomized restarts, and auto-adaptive multioperator recombination.
Comparative analysis conducted on 33 instances across 18 DTLZ, WFG, and CEC 2009 test problems.
Performance evaluated using Latin hypercube sampling and 50 replicate random seed trials.

Main Results:

The Borg MOEA demonstrates competitive or superior performance compared to six state-of-the-art MOEAs on the majority of tested problems.
The study validates the effectiveness of the unified framework and its components.

Conclusions:

The Borg MOEA represents a class of adaptive algorithms, crucial for tackling complex many-objective problems.
Adaptive operator selection is vital for enhancing search capabilities in challenging optimization landscapes.