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An orthogonal multi-objective evolutionary algorithm for multi-objective optimization problems with constraints.

Sanyou Y Zeng¹, Lishan S Kang, Lixin X Ding

¹Dept. of Computer Science and Technology, China University of GeoSciences, Wuhan 430074, Hubei, P. R. China. sanyou-zeng@263.net

Evolutionary Computation

|April 21, 2004

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An orthogonal multi-objective evolutionary algorithm (OMOEA) efficiently solves constrained multi-objective optimization problems (MOPs). This deterministic approach achieves high-precision, uniformly distributed Pareto-optimal solutions, outperforming existing methods.

Area of Science:

Computational Intelligence
Optimization Algorithms
Evolutionary Computation

Background:

Multi-objective optimization problems (MOPs) present challenges in finding optimal solutions.
Existing multi-objective evolutionary algorithms (MOEAs) often struggle with constrained problems.
Incorporating constraints early in the dominance relation is crucial for effective MOPs.

Purpose of the Study:

To propose a novel orthogonal multi-objective evolutionary algorithm (OMOEA) for constrained MOPs.
To develop a deterministic search strategy that avoids randomness and blindness.
To enhance the precision and distribution of Pareto-optimal solutions.

Main Methods:

Constraints are integrated into the Pareto dominance determination for a strict partial order.

Related Experiment Videos

Orthogonal design and statistical optimization methods are generalized for MOPs.

A niche-splitting mechanism creates a deterministic, exponentially increasing search space exploration.

Main Results:

The OMOEA demonstrates superior performance compared to established algorithms like NSGAII and SPEA2.
It achieves high precision and uniform distribution of solutions for benchmark and engineering problems.
The algorithm successfully identified the previously unknown Pareto-optimal set for the engineering problem W.

Conclusions:

OMOEA offers a robust and efficient method for solving constrained MOPs.
Its deterministic nature and effective search strategy lead to superior solution quality.
The algorithm shows significant potential for complex engineering optimization tasks.