Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

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Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA.

It is of general agreement that quality issues should be considered very early in the software development process, to mitigate risks and to facilitate the achievement of the overall software system. Moreover, the architecture of the system drives the whole development process. The fulfillment of nonfunctional quality requirements by a candidate architecture is crucial to select the convenient architecture on which the whole system will be articulated. This issue is very important in the construction of reliable evolutionary applications. Software development methods do not give many details on this important stage. This work deals with the specification of quality requirements for software architecture, introducing a technique based on the ISO 9126-1 standard. The quality characteristics of the ISO quality model are refined into attributes, which can be measured to enrich the information about the architecture. Our technique is used to help selecting a suitable architecture among a set of candidates, by comparing the values of the respective quality attributes. A case study illustrates the application of the technique on a monitoring system. Our approach facilitates the choice of the right decisions during the architecture analysis process. It could be easily integrated into a general software development process or into specific architectural design methods.

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