Salt gradient solar ponds (SGSPs) are a highly economical and sustainable renewable energy technology. However, their optimal design is not trivial due to often conflicting objectives presented in their intended utility, environmental operational settings, and stability requirements. Here, we present the problem of finding the optimal salt density profiles (SDPs) as a vital aspect of SGSP design as a many‐objective optimization problem. We then propose a systematic integer‐coded, order‐preserving mutation and crossover evolutionary operator‐based nondominated sorting genetic search algorithm (ONSGA) that automatically generates optimal SDPs. Specifically, the proposed method aims to maximize solar insolation transmission and thermal efficiency while minimizing changes in SDP and total salt consumption, all while meeting the overall criterion for pond stability. The proposed approach is general; in addition to the mentioned criteria, any other relevant criteria, such as climatic conditions, could also be considered. The proposed approach is compared with the traditional multiobjective covariance adaptive evolution strategy (MOCMA) and the competitive and cooperative swarm optimization constrained multiobjective optimization algorithm (CMOCSO) in terms of hypervolume, spread, and Pareto optimal solutions, using the initial SDP measured at the RMIT SGSP pond and studied under five typical initial temperature scenarios. The proposed algorithm outperforms the others in optimizing the SDPs, achieving 45 wins for hypervolume and 43 wins for spread across 65 experiments. Additionally, the optimal profiles yield improvements of up to 94% in some aspects over the currently implemented SDPs. The variety of optimal solutions also confirms that optimal profiles are not unique, and different in‐situ criteria surrounding a given solar pond lead to other desirable profiles.
Rafiei et al. (Thu,) studied this question.