Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this work, the unique strategies of giant trevally when hunting seabirds are mathematically modeled and are divided into three main steps. In the first step, the foraging movement patterns of giant trevallies are simulated. In the second step, the giant trevallies choose the appropriate area in terms of food where they can hunt for prey. In the last step, the trevally starts to chase the seabird (prey). When the prey is close enough to the trevally, the trevally jumps out of the water and attacks the prey in the air or even snatches the prey from the water surface. The performance of GTO is compared against state-of-the-art metaheuristics for global optimization on a set of forty benchmark functions with different characteristics and five complex engineering problems. The comparative study, scalability analysis, statistical analysis based on the Wilcoxon rank sum test, and the findings suggest that the proposed GTO is an efficient optimizer for global optimization. Note that the MATLAB source codes for GTO will be publicly available after the acceptance of the paper.

In science and engineering, many optimization tasks are difficult to solve, and the core concern these days is to apply metaheuristic (MH) algorithms to solve them. Metaheuristics have gained significant attention in recent years, with nature serving as the fundamental inspiration where self-organization property led to collective intelligence emerging from the behavior of a swarm of birds or colony of insects or more and more natural behavior. These swarms or colonies, even with extremely low individual competence, have the ability to accomplish many complicated activities that can be considered necessary for their existence. Accordingly, many MH algorithms have been developed based on natural phenomena. In this article, an analysis review of more than one hundred metaheuristics have been made. Further, the main contributions of this article are to give some vital insights about metaheuristics, presenting and proposing the general mathematical framework of MH algorithms and dividing it into a number of tasks with possible progress for each task. While there are still many open issues in this field, it is worth noting that there have been significant advancements in recent years. As a result, new algorithms are continuously being proposed to address these challenges.

Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

Mimicking natural phenomenon of social insects, such as bird flocks and insect colonies by merging randomness facility and some other simulation rules, are the core tasks of the artificial meta-heuristic algorithms. Such algorithms are the most efficient and powerful techniques used to solve various complicated real-world optimization problems. Firefly algorithm, which belongs to nature meta-heuristics algorithms, is inspired by mating and flashing behavior or the phenomenon of bioluminescent communication of fireflies in the nature. In this paper, a hardware structure design for firefly algorithm has been proposed. Firefly algorithm is executing sequentially as all meta-heuristic algorithms, due to the nature of the algorithm. Therefore, sequential hardware structure design for the algorithm using Finite State Machine (FSM) system has been proposed. The hardware design structure implementation is mapped into a FPGAs (SPARTAN 3XS1600) device. Numerical results of the comparison between the hardware and the software (using C++ programming language) implementation of Firefly algorithm were obtained. These results indicate that the hardware implementation is executed 461 times faster than the software implementation. Indeed, the required execution time for finding the optimal solution can be reduced rapidly using the proposed hardware design structure.

Global optimization has been used in many real-world problems. Nature-inspired meta-heuristic algorithms, such as the Northern Goshawk Optimization NGO algorithm that has just been proposed, are often used to solve these kinds of optimization problems. An NGO provides satisfactory results. In this algorithm, the proposed exploration model may not provide sufficient coverage of the problem space, trapping the system in a local optimal solution. To improve the performance of NGO, a novel and efficient improved northern goshawk optimization technique named INGO is proposed in this paper. In INGO, a new concept of switching between exploration and exploitation has been developed to improve overall algorithm performance to avoid being stuck in local optima. Also, to increase search capabilities, Levy Flight is used. Twenty-three known benchmark functions were used to test the performance of the proposed INGO. The results were compared to those of an NGO and some well-known robust algorithms. Experimental data indicates that the INGO suggested in this study consistently outperforms the traditional NGO and alternative methods in a significant number of test functions.