Estimates of genetic and environmental variability in soybeans (Glycine max. L.) [Egypt].

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

The detection and processing of painful stimuli in afferent sensory neurons is critically dependent on a wide range of different types of voltage- and ligand-gated ion channels, including sodium, calcium, and TRP channels, to name a few. The functions of these channels include the detection of mechanical and chemical insults, the generation of action potentials and regulation of neuronal firing patterns, the initiation of neurotransmitter release at dorsal horn synapses, and the ensuing activation of spinal cord neurons that project to pain centers in the brain. Long-term changes in ion channel expression and function are thought to contribute to chronic pain states. Many of the channels involved in the afferent pain pathway are permeable to calcium ions, suggesting a role in cell signaling beyond the mere generation of electrical activity. In this article, we provide a broad overview of different calcium-permeable ion channels in the afferent pain pathway and their role in pain pathophysiology.

Calcium-Permeable Ion Channels in Pain Signaling

Lack of analgesic effect of opioids on neuropathic and idiopathic forms of pain

Common Brain Mechanisms of Chronic Pain and Addiction

This paper presents a new optimization algorithm based on human society’s intelligent contests. FIFA World Cup is an international association football competition competed by the senior men’s national teams. This contest is one of the most significant competitions among the humans in which people/teams try hard to overcome the others to earn the victory. In this competition there is only one winner which has the best position rather than the others. This paper introduces a new technique for optimization of mathematic functions based on FIFA World Cup competitions. The main difficulty of the optimization problems is that each type of them can be interpreted in a specific manner. World Cup Optimization (WCO) algorithm has a number of parameters to solve any type of problems due to defined parameters. For analyzing the system performance, it is applied on some benchmark functions. It is also applied on an optimal control problem as a practical case study to find the optimal parameters of PID controller with considering to the nominal operating points $$(K_{g}$$
 , $$T_{g})$$
 changes of the AVR system. The main objective of the proposed system is to minimize the steady-state error and also to improve the transient response of the AVR system by optimal PID controller. Optimal values of the PID controller which are achieved by WCO algorithm are then compared with particle swarm optimization and imperialist competitive algorithm in different situations. Finally for illustrating the system capability against the disturbance, it is applied on a generator with disturbance on it and the results are compared by the other algorithms. The simulation results show the excellence of WCO algorithm performance into the nature base and other competitive algorithms.

A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System

There are great deals of consumer photographs which are affected by red-eye artifacts and arise frequently when shooting with flash. In this paper, a new technique is proposed to solve this problem. The proposed technique starts by detecting the skin-like regions using an optimized pixel-based neuro-fuzzy processing; morphological operations are then used to discard the extra areas after crossing the threshold. Once the skin regions are detected, five new features including geometric and color metrics are proposed to enhance the classification accuracy of the red-eye artifacts. After that, another optimized neuro-fuzzy classifier is employed to classify the red-eye regions by using the presented features. Final result is achieved by a definite syntax between skin and red-eye regions, and then, a simple correction method is used to correct the detected regions. Finally, a comparison is performed among the proposed method toward the other popular procedures and also a simple neuro-fuzzy. Final results showed the high performance of the proposed method.

Imperialist Competitive Algorithm-Based Optimization of Neuro-Fuzzy System Parameters for Automatic Red-eye Removal

Melanoma is one of the most dangerous tumors in the human kind cancers. Nonetheless, early detection of this cancer can help the doctors to cure it perfectly. In this paper, a new efficient method is proposed to detect the malignant melanoma images from the images. In the proposed method, a hybrid technique is utilized. We first eliminate the extra scales by using edge detection and smoothing. Afterwards, the main hybrid technique is applied to segment the cancer images. Finally by using the morphological operations, the extra information is eliminated and used to focus on the area which melanoma boundary potentially exists. Here, Gray Wolf Optimization algorithm is utilized to optimize an MLP neural Networks (ANN). Gray Wolf Optimization is a new evolutionary algorithm which recently introduced and has a good performance in some optimization problems. GWO is a derivative-free, Meta Heuristic algorithm, mimicking the ecological behaviour of colonizing weeds. Gray wolf optimization is a global search algorithm while gradient-based back propagation method is local search. In this proposed algorithm, Multi-Layer Perceptron Network (MLP) employs the problem's constraints and GWO algorithm tries to minimize the root mean square error. Experimental results show that the proposed method can develop the performance of the standard MLP algorithm significantly.

/pdf/a-hybrid-neural-network-gray-wolf-optimization-algorithm-for-2mv49qsbak.pdf

A hybrid neural network-gray wolf optimization algorithm for melanoma detection

The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies.

A New Improved Model of Marine Predator Algorithm for Optimization Problems

In recent years, energy demand has grown significantly relative to its production. The power companies have also offered a variety of schemes such as energy consumption management to meet this growing consumer demand. Energy consumption management is a set of strategies used to optimize energy consumption which includes a set of interconnected activities between the utility and customers to transfer the load from peak hours to off-peak hours. This reduces the electricity bill. This paper presents an optimal schedule for the consumption of residential appliances based on improved multi-objective antlion optimization algorithm to minimize the electrical cost and the user comfort. To prevent peaks, the peak-to-average ratio is considered as a constraint for the energy cost function. Also, two different tariff signals have been used to measure energy costs. The real-time pricing and critical peak pricing are considered as energy tariffs. The simulations results are compared with other meta-heuristic algorithms, including multi-objective particle swarm optimization, the second version of the non-dominated sorting genetic algorithm, and the basic antlion optimizer algorithm to show the superiority of the proposed algorithm. Final results show that using the proposed scheme reaches electricity bills less than 80%.

/pdf/a-new-optimal-energy-management-strategy-based-on-improved-53t3gdlue9.pdf

Mehdi Ramezani

Papers

A New Meta-Heuristic Optimization Algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System

Imperialist Competitive Algorithm-Based Optimization of Neuro-Fuzzy System Parameters for Automatic Red-eye Removal

A hybrid neural network-gray wolf optimization algorithm for melanoma detection

A New Improved Model of Marine Predator Algorithm for Optimization Problems

A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home