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Analysis of forest structure is essential for enhanced understanding of forest ecology and management. In this study, the spatial structure of the existing species in Perk district of Ghaleh Gol site in Lorestan Province was explored. To this end, a 32-hectare region was 100% surveyed. To investigate the spatial structure, we used a set of indices including the Clark and Evans, uniform angles, Shannon-Wiener, mingling, Crown canopy and Crown canopy differentiation indices. The results showed the average values of 0.8 and 0.47 for Clark and Evans and uniform angles indices, respectively. This indicated random and cluster distribution patterns. In addition, Mean values of 0.25 and 0.06 were returned by Shannon - Weiner and mingling indices. Due to the dominant Oak coverage within the study area, Blend Low Index was additionally calculated. The mean Crown Canopy Index of 0.5 turned out a canopy dominance of Quercus brantii, Acer cineracense, Crataegus sp. and Pyrus syriaca over Lonicera nomularifolia and Amygdalus sp.. Moreover, the Crown Canopy Differentiation Index was calculated to quantify the differences between the levels of crown canopy in adjacent trees. This returned a mean value of 0.48 for the entire trees, which reflects the difference between the tree canopy levels. The results showed that the study site is currently undergoing an inappropriate biodiversity for woody species which yet shows better conditions compared to similarly-structured stands in the region. As a conclusion, proper forest planning measures should be carried out to prevent the recently experienced consistent loss of biodiversity.

Multiobjective density driven evolutionary algorithm (MODdEA) has been quite successful in solving multiobjective optimization problems (MOPs). To further improve its performance and address its deficiencies, this paper proposes a hybrid evolutionary algorithm based on dimensional diversity (DD) and firework explosion (FE). DD is defined to reflect the diversity degree of population dimension. Based on DD, a selection scheme is designed to balance diversity and convergence. A hybrid variation based on FE and genetic operator is designed to facilitate diversity of population. The proposed algorithm is tested on 14 tests problems with diverse characteristics and compared with three state-of-the-art designs. Experimental results show that the proposed design is better or at par with the chosen state-of-the-art algorithms for multiobjective optimization. [ABSTRACT FROM AUTHOR]