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

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.

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

“…One issue with this approach is that no solution can be achieved if the goals are set unreasonably by DMs for MOPs with a discontinuous PF. To address this issue, an algorithm is proposed in [115] that divides the constraints into hard and soft constraints according to the priority of objectives.…”

MOCCA-II: A multi-objective co-operative co-evolutionary algorithm

“…The sharing function is defined as 2. N2: The dynamic sharing proposed by [26] employs sharing radius defined as in Eq. (9) and the sharing function and niche count function calculated by Eqs.…”

A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment

“…When the archive is full and the added individual is non-dominated, it replaces an archive member in the most crowded region in objective space. A canonical sharing function is used to determine the degree of crowdedness and dynamic sharing [46] is used to calculate the sharing radius adaptively.…”