A Collapse-Resistant Red-Billed Blue Magpie Optimizer for Employment-Policy Design: Programme Targeting and Occupational Retraining Pathways

Abstract

Improvements to bio-inspired optimisers usually answer generic defects with generic operators. This paper instruments the red-billed blue magpie optimizer (RBMO) and measures what fails. Both operators scale their step by differences between population positions, and the attack places every candidate at the incumbent best, so the search scale shrinks with the spread the attack destroys. On five instrumented functions of the IEEE Congress on Evolutionary Computation 2017 (CEC2017) benchmark suite the population collapses to numerical identity, and on four the error stagnates over the last 28% to 58% of the run. The proposed collapse-resistant RBMO (CRBMO) breaks the loop with a dual-anchor attack, a radius control under the one-fifth success rule and an adaptive foraging allocation; it adds no chaotic map, Levy flight or opposition-based learning. On CEC2017 at 30,000 evaluations, against nine swarm algorithms, it attains the best Friedman rank at <!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ -->