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Swarm Intelligence algorithms like PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), Glow-worm swarm Optimization, etc. have been utilized by researchers for solving optimization problems. This work presents the application of a novel modified EHO (Elephant Herding Optimization) for cost optimization of shell and tube heat exchanger. A comparison of the results obtained by EHO in two benchmark problems shows that it is superior to those obtained with genetic algorithm and particle swarm optimization. The overall cost reduction is 13.3 % and 9.68% for both the benchmark problem compared to PSO. Results indicate that EHO can be effectively utilized for solving real-life optimization problems.