This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.

Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems

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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Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Orthogonal frequency division multiplexing (OFDM) is a modulation technique which is now used in most new and emerging broadband wired and wireless communication systems because it is an effective solution to intersymbol interference caused by a dispersive channel. Very recently a number of researchers have shown that OFDM is also a promising technology for optical communications. This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications. To achieve good performance in optical systems OFDM must be adapted in various ways. The constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless are discussed and the new forms of optical OFDM which have been developed are outlined. The main drawbacks of OFDM are its high peak to average power ratio and its sensitivity to phase noise and frequency offset. The impairments that these cause are described and their implications for optical systems discussed.

OFDM for Optical Communications

Adaptive repair algorithm for TORA routing protocol based on flood control strategy

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.

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A comprehensive survey of sine cosine algorithm: variants and applications

Crow Search Algorithm (CSA) is a recent swarm intelligence optimization algorithm inspired by the social intelligent behavior of crows for hiding food. It has been widely used to solve a large variety of optimization problems in several fields and areas of research and has proved its efficiency compared to several state-of-the-art optimization algorithms available in the literature. This paper presents a comprehensive overview of Crow Search Algorithm and its new variants categorized into modified and hybridized versions. It also describes the several applications of CSA in various domains such as feature selection, image processing, scheduling, economic dispatch, distributed generation, and other engineering problems. In addition, the paper suggests some interesting research areas related to CSA enhancement, CSA hybridization, and possible new applications.

Asma Benmessaoud Gabis

Papers

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

A comprehensive survey of sine cosine algorithm: variants and applications

A comprehensive survey of Crow Search Algorithm and its applications

NoC routing protocols – objective-based classification

A Survey of Channel Modeling Techniques for Visible Light Communications