An Evolutionary Algorithm with Advanced Goal and Priority Specification for Multi-objective Optimization

TL;DR

This paper introduces an advanced evolutionary algorithm for multi-objective optimization that incorporates goal-sequence domination, priority handling, and dynamic sharing to improve search diversity and solution quality.

Contribution

It proposes a novel goal-sequence domination scheme with priority and constraint handling, plus a dynamic sharing method, enhancing multi-objective evolutionary optimization capabilities.

Findings

Achieved diverse and uniformly distributed Pareto fronts.

Demonstrated superior performance in practical servo control system design.

Reduced computational effort compared to existing methods.

Abstract

This paper presents an evolutionary algorithm with a new goal-sequence domination scheme for better decision support in multi-objective optimization. The approach allows the inclusion of advanced hard/soft priority and constraint information on each objective component, and is capable of incorporating multiple specifications with overlapping or non-overlapping objective functions via logical 'OR' and 'AND' connectives to drive the search towards multiple regions of trade-off. In addition, we propose a dynamic sharing scheme that is simple and adaptively estimated according to the on-line population distribution without needing any a priori parameter setting. Each feature in the proposed algorithm is examined to show its respective contribution, and the performance of the algorithm is compared with other evolutionary optimization methods. It is shown that the proposed algorithm has performed well in the diversity of evolutionary search and uniform distribution of non-dominated individuals along the final trade-offs, without significant computational effort. The algorithm is also applied to the design optimization of a practical servo control system for hard disk drives with a single voice-coil-motor actuator. Results of the evolutionary designed servo control system show a superior closed-loop performance compared to classical PID or RPT approaches.