The micro immune optimization approach: solving constrained nonlinear interval number programming

摘要

This work investigates a bio-inspired micro immune optimization approach for solving constrained interval number programming, after analyzing the relation between solutions for one such programming problem and its natural interval extension optimization. The approach executes two tasks of optimization. The first task collects some efficient solutions of the latter problem after population evolution. The second task decides the bounds of objective intervals of the solutions, relying upon a simple static search procedure. Thereafter, optimal solution(s) can be found from these solutions, in terms of the acquired theoretical results. The numerical experiments have illustrated that the present approach is a competitive and highly-efficient optimizer.