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The JAYA is classified as the state-of-the-art population-oriented algorithm for the optimization of diverse problems, both discrete and continuous. The concept behind this algorithm is to present a solution by means of the best and worst individuals in the population. On the other hand, teaching–learning-based optimization algorithm cooperation of a teacher on students' learning process. Due to each one having some benefits and drawbacks, combining those leads to better exploring the problem. Consequently, this investigation exploits the hybridization of both mentioned algorithms, and a novel algorithm is made named H-JTLBO (hybridization of JAYA and teaching learning-based optimization). The proposed approach is then evaluated using different test functions used frequently in the literate. Finally, the results of such functions are compared with other optimization algorithms which have recently been introduced in the literature, such as Sine Cosine Algorithm (SCA), Grasshopper Optimization Algorithm (GOA), Moth-flame optimization (MFO), and JAYA algorithm. In addition, the statistical test is used to evaluate the proposed method. Through the results, H-JTLBO outperforms all mentioned algorithms in terms of convergence and solution quality. [ABSTRACT FROM AUTHOR]

Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. JAYA algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of Jaya to present a high-efficiency version named Powerful Enhanced Jaya (PEJAYA). In other words, the methodology of updating position in Jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine–cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have shown promising results in solving various optimization problems. The crow search algorithm (CSA) is a new and effective meta-heuristic algorithm that emulates crows' intelligent group behavior in nature. However, it suffers from several problems, such as trapping into local optimum and premature convergence. This paper proposes an improved crow search algorithm (ICSA), which has been tested and evaluated by a set of well-known benchmark functions. A new update mechanism that uses the merits of the global best position to move toward the best position is proposed. This mechanism increases the convergence of the algorithm and improves its local search-ability. Twenty benchmark functions are used to evaluate the performance of the proposed ICSA. Moreover, the ICSA algorithm is compared with the conventional CSA and other meta-heuristic algorithms such as particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), gray wolf optimizer (GWO), moth-flame optimization (MFO), and sine-cosine algorithm (SCA). The experimental result shows that the proposed ICSA algorithm has produced promising results and outperformed conventional CSA and other meta-heuristic algorithms. Also, the proposed ICSA has a more robust convergence for optimizing objective functions in terms of solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Harmony search (HS) is a type of population-based optimization algorithm that is introduced based on the idea of musical instruments being tuned to obtain the best harmony state. Several versions of HS have been presented, with global harmony search (GHS) considered one of the most popular. Although GHS is efficient in solving various optimization problems, a new position updating mechanism has been added to improve its efficiency and help it avoid getting stuck in local minima. The novel algorithm proposed in this paper is called the intersect mutation global harmony search algorithm (IMGHSA), which has been tested and evaluated on a set of well-known benchmark functions. The IMGHSA is compared with several improved variants of the HS algorithm, such as the basic version of harmony search (HS), improved differential harmony search, generalized opposition-based learning with global harmony search , and novel global harmony search. The experimental results show that the proposed IMGHSA performs better than the state-of-the-art HS variants and has a more robust convergence when optimizing objective functions in terms of the solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Background: Shell fragility of hatching eggs can have negative implications on the economic performance of hatcheries. Objective: To determine the efficacy of sealing eggshell micro cracks with either coloured or uncoloured nail varnish, and molten paraffin on hatchability, embryonic mortality (EM) and hatched chick weight (CW). Methods: Eggs (n= 576) with micro-cracks were assigned among four groups (n=144 each group) for a 21 d incubation period. One group was untreated (CE). In the other groups, the micro cracked area of eggshells was sealed with uncolored nail varnish (NV), colored nail varnish (CV), or molten paraffin wax (MP). A positive control group of un-cracked eggs (UE) was also included (n= 144). Results: The eggshell sealant treatments allowed normal conductance related to egg weight loss after 18 d of incubation (11.45%), and chick weights were normal among treatment groups (44.7 g). Hatchability and embryonic mortality in the early and late incubation periods of the NV group was similar to UE (84.02 vs 86.11% for hatchability, 6.95 vs 10.42% for EM on days 1-10, and 2.08 vs 1.39% for EM on days 18-21 respectively; p>0.05). The CV group had lower hatchability than the NV (77.77 vs 84.02% respectively; p<0.05), whereas MP showed similar hatchability compared to the CE group (59.72 vs 72.92%, respectively p>0.05). Conclusions: Application of uncoloured nail varnish on shell micro-cracks improves egg hatchability. [ABSTRACT FROM AUTHOR]

The effect of in ovo administration of different levels of betaine and choline on egg hatchability, immune response, growth and carcass traits of broiler chickens was studied. Four thousand hatching eggs from Ross 308 broiler breeder layers, weighed individually, were incubated for 21 days in a commercial hatchery. At 12th day of incubation, 3456 fertilized eggs were randomly divided into 8 experimental groups of 3 replicates each (144 eggs per replicate): negative control (NC) - not injected; positive control (PC) - injected with 0.5 mL deionized water; Bet 0.25 - injected with 0.5 mL deionized water+0.25 mg soluble betaine; Bet 0.375 - injected with 0.5 mL deionized water+0.375 mg soluble betaine; Bet 0.50 - injected with 0.5 mL deionized water+0.50 mg soluble betaine; Chol 0.25 - injected with 0.5 mL deionized water+0.25 mg soluble choline; Chol 0.375 - injected with 0.5 mL deionized water+0.375 mg soluble choline; Chol 0.50 - injected with 0.5 mL deionized water+0.50 mg soluble choline. Among the hatched chickens, 360 males were randomly chosen (45 for each group) and were grown up to 42nd day of age. The embryo mortality, pecked eggs, infected eggs and hatchability percentages were similar among the experimental groups. The betaine and choline treatments improved hatching weight and final weight of chickens, while reduced feed conversion ratio and abdominal fat percentage. No effect on carcass yield, and breast muscle, leg and wings percentages, as well as on immunoglobulin M (IgM), G (IgG), and total antibody (IgT) titers was observed. The treatments had little effect on internal organs. [ABSTRACT FROM AUTHOR]

Since real-world problems are quite complex, developing more effective methods to optimize them is taken into consideration at the present age. One of the most popular strategies to solve such problems is nature-inspired metaheuristic algorithms which have presented promising solutions in different fields. The JAYA algorithm is an innovative and promising technique for solving a wide range of optimization problems. Hence, this investigation provides a new hybrid algorithm based on JAYA and crow search algorithm (CSA), known as HJCSA, to enhance its performance. In other words, the mechanism of updating position in CSA is integrated with JAYA algorithm to reinforce the convergence and search capabilities of JAYA algorithm. The proposed hybrid algorithm is evaluated through solving 20 well-known benchmark functions, and obtained results are compared with various up-to-date algorithms, including CSA, JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO), and sine–cosine algorithm (SCA). In addition, a real-world engineering problem is optimized by the proposed optimization approach and compared with other published works. The results showed that the suggested optimization algorithm is much more vigorous in escaping from local minima, possessing better convergence, and discovering more accurate solutions. [ABSTRACT FROM AUTHOR]