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

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 D = 30, 50 and 100 and 1.72 overall against 3.16 for RBMO. It outranks three published RBMO variants in every dimension, but the covariance matrix adaptation evolution strategy (CMA-ES) and the single-objective differential evolution algorithm jSO outrank it overall. At the standard CEC2017 budget its lead over RBMO is not significant; success-history-based adaptive differential evolution with linear population size reduction (L-SHADE), jSO and CMA-ES outrank it. Applied to programme targeting and retraining pathway design in employment policy, CRBMO beats RBMO in every instance, though the crested porcupine optimizer (CPO) ranks first.