AMOAIA: Adaptive Multi-objective Optimization Artificial Immune Algorithm.

An adaptive multi-objective optimization artificial immune algorithm (AMOAIA) is presented in this paper. An innovating sorting mechanism based on its Pareto ratio is used to sort individuals in the antibody population. The selection and cloning scheme is improved by using a neighborhood-based fitness assessment. An adaptive clone selection mechanism is introduced to preserve the diversity of the antibody. A new hybrid mutation operator using chaos random series for globally optimization solution has been proposed to maintain the diversity of the antibody population. A multi-objective optimization clustering algorithm based on the distribution of distributed Pareto frontiers is proposed. In addition, the effectiveness of the proposed algorithm is verified under many difficult conditions such as local optimality, non-uniformity, discontinuity, non-convexity, high-dimension, and constraints. The comparative study shows the effectiveness of the proposed algorithm, which produces solution sets that are highly superiority in terms of global convergence, diversity and distribution. [ABSTRACT FROM AUTHOR]