Nature inspired meta heuristics like swarm intelligence (SI), Artificial neural networks (ANN), evolutionary computing (EC) etc. have been used by researchers to solve single and multi-objective optimization problems of different fields. This work uses a novel α-SOS (Adaptive symbiotic organisms search) algorithm for cost optimization of shell and tube heat exchanger. This algorithm is implemented for cost optimization of two benchmark STHX problem which are used by several researchers. Validation of the results is presented by comparing the geometric, flow and operational parameters of the same design problems when solved using particle swarm optimization (PSO), Alpha tuned elephant herding optimization technique (α-EHO) and Gravitation search algorithm (GSA). Result indicates a 4.73% and 11.3% reduction in cost for both the case study respectively when compared to same problems solved using PSO. Although when comparing with α-EHO, results does not indicate any substantial difference. Furthermore, operational, and geometric dimensions are also calculated. This algorithm can be eventually applied to real world design engineering problems.