Multi-Objective Optimization of Solar Air Heaters Using Particle Swarm Optimization and Genetic Algorithm: A Review

This paper reviewed the thermo-hydraulic performance characteristics of artificially roughened solar air heaters and the application of Particle Swarm Optimization and the Genetic Algorithm to their multi-objective design optimization. The physical mechanism underlying the trade-off — artificial roughness disrupting the laminar sub-layer to enhance convective heat transfer while simultaneously increasing form drag and hence friction factor — was discussed together with the well-documented optimum roughness pitch and angle-of-attack values reported across the literature. The inherent conflict between thermal efficiency and friction factor / pumping power was highlighted as the central driver of the multi-objective formulation, and the working principles, relative strengths, and typical implementations of PSO and GA were summarized alongside related metaheuristics and hybrid approaches. An illustrative case study demonstrated that both algorithms, under matched evaluation budgets, converge to closely matching Pareto fronts, with PSO showing faster early convergence and GA offering marginally broader exploration and a monotonic, elitism-guaranteed improvement path. Overall, the difference in final solution quality between the two algorithms was found to be small for the smooth, low-dimensional objective landscape typical of SAH roughness-geometry optimization, suggesting that either algorithm is a reasonable default choice for this problem class, with the final selection more usefully guided by considerations such as available evaluation budget, ease of implementation, and familiarity of the design team with each method. Key research gaps — particularly the limited use of true Pareto-based multi-objective algorithms, the scarcity of experimentally validated multi-objective studies, the restriction of most existing work to two objectives, and the limited attention paid to alternative duct configurations and design robustness — were identified as promising directions for future work. Addressing these gaps would help move the field from largely correlation-based, single-weighting optimization studies toward a more complete, experimentally grounded, and decision-support-oriented treatment of solar air heater design.

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