Converting solar energy to electricity efficiently requires an accurate model with well-estimated parameters in a photovoltaic (PV) system. However, identifying parameters for the PV model remains challenging attributed to the nonlinearity and multimodality inherent in diode circuit models. In this paper, we propose a dual-feedback adaptive clone selection algorithm with a golden sine search. The clonal selection algorithm (CSA), known for its inherent diversity maintenance ability, offers an advantage in handling multimodal optimization problems. The critical factor is the cloning operator, particularly the determination of clone size, crucial for balancing exploration and exploitation. To address the conflict between clone size and convergence speed, we introduce a dual feedback adaptive cloning strategy. This strategy considers both the fitness difference between the hypermutated population and the original population and the harmonic mean distance of the population. Additionally, to improve accuracy, we introduce an elite learning strategy based on the golden sine mechanism. The proposed algorithm’s effectiveness is assessed through comprehensive experiments on PV cells and three PV modules, considering both single diode and double diode models, respectively. The experimental results demonstrate that the accuracy of parameter identification achieved by the proposed algorithm exceeds 99.9% for all three types of PV modules. Moreover, we validate the algorithm’s performance using manufacturer’s datasheets with varying irradiations and temperatures. All experiments consistently indicate the stability and effectiveness of the proposed method in accurately identifying the parameters of PV models.

In practical engineering, multi-objective optimization often encounters situations where multiple Pareto sets (PS) in the decision space correspond to the same Pareto front (PF) in the objective space, known as Multi-Modal Multi-Objective Optimization Problems (MMOP). Locating multiple equivalent global PSs poses a significant challenge in real-world applications, especially considering the existence of local PSs. Effectively identifying and locating both global and local PSs is a major challenge. To tackle this issue, we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded, promising regions and regulate the number of offspring in areas that have been thoroughly explored. This approach achieves a balanced trade-off between exploration and exploitation. Furthermore, we present an interval allocation strategy that adaptively assigns fitness levels to each antibody. This strategy ensures a broader survival margin for solutions in their initial stages and progressively amplifies the differences in individual fitness values as the population matures, thus fostering better population convergence. Additionally, we incorporate a multi-population mechanism that precisely manages each subpopulation through the interval allocation strategy, ensuring the preservation of both global and local PSs. Experimental results on 21 test problems, encompassing both global and local PSs, are compared with eight state-of-the-art multimodal multi-objective optimization algorithms. The results demonstrate the effectiveness of our proposed algorithm in simultaneously identifying global Pareto sets and locally high-quality PSs.

Intraspecific variability of Capila omeia (Leech, 1894) from Chongqing is investigated based upon the comparison of the 658 bp COI gene sequences and adult morphology. The morphological characters used for distinguishing C. pseudomeia Fan & Wang, 2004 from C. omeia are proved to be in the range of individual variation of the latter. Therefore, C. pseudomeia syn. n. is treated as a junior subjective synonym of C. omeia. Adults and genitalia of both sexes of C. omeia are illustrated. A distribution map of the species is provided as well.

Despite the conspicuous different appearances in the males, Capila jayadeva Moore, 1866, C. translucida (Leech, 1893), C. hainana mackwoodi (Evans, 1914) and C. barroni (Riley & Godfrey, 1921) are proved to be conspecific based on an analysis of the 658 bp COI gene sequences, and the result is supported by the genitalia morphology. Considering the distinguishable wing patterns in the male and the allopatric distribution, the following subspecific combinations are proposed: C. jayadeva translucida, C. jayadeva mackwoodi, C. jayadeva barroni and C. jayadeva arooni Eliot, 1987. Therefore, with the nominate subspecies and C. jayadeva khambounei Uehara, 2024, the sexually dimorphic C. jayadeva currently includes six subspecies.