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This paper introduces a new metaheuristic algorithm called Migration Algorithm (MA), which is helpful in solving optimization problems. The fundamental inspiration of MA is the process of human migration, which aims to improve job, educational, economic, and living conditions, and so on. The mathematical modeling of the proposed MA is presented in two phases to empower the proposed approach in exploration and exploitation during the search process. In the exploration phase, the algorithm population is updated based on the simulation of choosing the migration destination among the available options. In the exploitation phase, the algorithm population is updated based on the efforts of individuals in the migration destination to adapt to the new environment and improve their conditions. MA’s performance is evaluated on fifty-two standard benchmark functions consisting of unimodal and multimodal types and the CEC 2017 test suite. In addition, MA’s results are compared with the performance of twelve well-known metaheuristic algorithms. The optimization results show the proposed MA approach’s high ability to balance exploration and exploitation to achieve suitable solutions for optimization problems. The analysis and comparison of the simulation results show that MA has provided superior performance against competitor algorithms in most benchmark functions. Also, the implementation of MA on four engineering design problems indicates the effective capability of the proposed approach in handling optimization tasks in real-world applications. [ABSTRACT FROM AUTHOR]

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications. [ABSTRACT FROM AUTHOR]

This paper presents a new evolutionary-based approach called a Subtraction-Average-Based Optimizer (SABO) for solving optimization problems. The fundamental inspiration of the proposed SABO is to use the subtraction average of searcher agents to update the position of population members in the search space. The different steps of the SABO's implementation are described and then mathematically modeled for optimization tasks. The performance of the proposed SABO approach is tested for the optimization of fifty-two standard benchmark functions, consisting of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types, and the CEC 2017 test suite. The optimization results show that the proposed SABO approach effectively solves the optimization problems by balancing the exploration and exploitation in the search process of the problem-solving space. The results of the SABO are compared with the performance of twelve well-known metaheuristic algorithms. The analysis of the simulation results shows that the proposed SABO approach provides superior results for most of the benchmark functions. Furthermore, it provides a much more competitive and outstanding performance than its competitor algorithms. Additionally, the proposed approach is implemented for four engineering design problems to evaluate the SABO in handling optimization tasks for real-world applications. The optimization results show that the proposed SABO approach can solve for real-world applications and provides more optimal designs than its competitor algorithms. [ABSTRACT FROM AUTHOR]

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

A new metaheuristic algorithm called green anaconda optimization (GAO) which imitates the natural behavior of green anacondas has been designed. The fundamental inspiration for GAO is the mechanism of recognizing the position of the female species by the male species during the mating season and the hunting strategy of green anacondas. GAO's mathematical modeling is presented based on the simulation of these two strategies of green anacondas in two phases of exploration and exploitation. The effectiveness of the proposed GAO approach in solving optimization problems is evaluated on twenty-nine objective functions from the CEC 2017 test suite and the CEC 2019 test suite. The efficiency of GAO in providing solutions for optimization problems is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that the proposed GAO approach has a high capability in exploration, exploitation, and creating a balance between them and performs better compared to competitor algorithms. In addition, the implementation of GAO on twenty-one optimization problems from the CEC 2011 test suite indicates the effective capability of the proposed approach in handling real-world applications. [ABSTRACT FROM AUTHOR]

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Populationbased optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution. [ABSTRACT FROM AUTHOR]

Optimization plays a key role in various disciplines of science in order to achieve the optimal solution among all available solutions. Innovation and contribution of this paper is in introducing a new optimization algorithm called Puzzle Optimization Algorithm (POA) to solve various optimization problems. The main idea in the design of the proposed POA is the mathematical simulation of the process of solving a puzzle as an evolutionary optimizer. The various steps of POA are explained and then its mathematical model is presented. The main advantage and feature of the proposed POA is that it has no control parameters and therefore does not require parameter setting. The performance of the proposed POA is tested on twenty-three different objective functions. Also, in order to analyse the performance of POA in optimization, its results are compared with eight other optimization algorithms. The optimization results indicate the high and acceptable ability of the proposed POA to solve optimization problems. In addition, the simulation results show that the proposed POA is far better and much more competitive than the eight compared optimization algorithms. [ABSTRACT FROM AUTHOR]

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design's central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke's law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive. [ABSTRACT FROM AUTHOR]

This paper presents a compact modeling approach of continuum robotic arms, with limited computations for faster numerical calculations in real-time implementations. It is assumed that the arm includes a backbone made of elastic rods, and the Cosserat rod theory is simplified for numerically faster modeling. The time-taking boundary-value problem (BVP) is detailed, and some numerical methods are introduced to solve the developed equations of modeling. Next, based on physical intuition, a fast and stable method is proposed to solve the BVP, as a moment-based approach. Also, a method to cancel some numerical integration errors with the least required computations and numerical efforts is presented. Next, a Jacobian-based control of multi-segment continuum robotic arms is developed. Finally, this procedure is experimentally verified to show the precision of proposed modeling approach and also to reveal the importance of faster solutions for real-time control. [ABSTRACT FROM PUBLISHER]

With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm's key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA's performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima. [ABSTRACT FROM AUTHOR]

Fluoride is one of the chemicals that enter the human body through drinking water and, at higher concentrations, can cause various long-term health effects. Owing to remove the pollutant from aqueous solutions on large-scale, researchers are always exploring efficient, inexpensive, and easily recoverable adsorbents. In this research study, a versatile magnetic biomaterial & chelating agent (Chitosan) is used as an adsorbent for fluoride removal. In the present research, chitin was extracted from discarded shrimp shells. The obtained Chitosan was characterized based on its yielding, moisture and ash content, degree of acetylation and water binding capacity according to standard protocols. Also, in this study, differential evolution optimization is used to evaluate isotherm and kinetic model parameters using nonlinear model expressions, thus depicting inherent mechanisms. The isotherm and kinetic studies revealed that the adsorption process follows Langmuir and the pseudo 2nd order kinetic model. The interactive effect of process variables is investigated, and their optimal conditions for higher removal efficiency are estimated using response surface methodology. A quadratic model was used to predict removal efficiency at different operating conditions, which resulted in R2 of 0.9949. Optimum adsorption efficiency (93.1%) was observed at an adsorbent rate of 1 g/L, initial concentration of 1 mg/L, pH of 3, and contact time of 25 min. The thermodynamic study is indicating that the process is exothermic and spontaneous. The effect of interventions such as chloride, acidic, and sulfate on fluoride removal was also investigated, and results indicated that Chitosan-Fe 3 O 4 is also efficient in their presence. A hybrid model of ANN and GA predicted the fluoride removal at different operating conditions, and results indicated that prediction values result in R2 of 0.9825. Thus, these results confirm the remarkable ability of Chitosan-Fe 3 O 4 nanocomposite for fluoride removal. • A magnetic versatile biomaterial & chelating agent was used for fluoride removal. • Chitosan was characterized based on its yielding, moisture, and degree of acetylation. • The nanocomposite properties were analyzed using BET, FESEM, FTIR, and XRD. • Performance of parameters estimated by linear and non-linear (DEO) approach. • The effect of interferers, such as sulfate, chloride, and humic acid anions was investigated. [ABSTRACT FROM AUTHOR]

Ever growing penetration of the behind-the-meter technologies is changing the electricity consumption profiles of end-users. Intelligent coordination of these emerging technologies through a robust communication infrastructure enables their seamless integration with electric utilities’ operation. In this context, an efficient and reliable communication infrastructure plays a pivotal role in enabling optimal integration of emerging resources. In this paper, we propose a game-theory based method to enhance efficiency of the underlying communication network. Specifically, we focus on Femtocell communication technology which is one the promising options for improving poor indoor communication coverage. The major drawback for using femtocell communication technology is cross-layer interference of femto users (FUs) and macro users (MUs) which adversely impact network performance. In this paper, we propose a novel approach for sharing spectrum in a cognitive radio system with FUs and MUs as primary and secondary users, respectively. The underlying problem is formulated as Stackelberg game that is joined with a convex optimization problem. In this study, MUs and FUs are assumed to be selfish, rational and motivated to achieve maximum utility function, while MUs are competing to obtain maximum bandwidth. Finally, we present a closed form solution for the proposed approach which obtains a unique Nash Equilibrium and prioritizes the access of MUs to femto-base stations. Simulation results provide proof of concept and verify the effectiveness of our mathematical modeling. [ABSTRACT FROM AUTHOR]

In the present work, the electrolyte equation of stateproposedby Huttenhuis et al. [Fluid Phase Equilibria2008, 264, 99–112] is improved todescribe the solubility of carbon dioxide in aqueous solutions of N-methyldiethanolamine (MDEA) in wide ranges of concentration,pressure, temperature, and acid gas loading. The molecular part ofthe equation is based on the modified Peng–Robinson Plus Associationequation of state (mPR-CPA EoS) proposed by Zoghi et al. [J. Pet. Sci. Eng.2011, 78, 109–118]. To account for the presence of ions, three contributionsare added to the mPR-CPA EoS to describe short-range interactions,long-range interactions, and the Born term. The same EoS is used forboth vapor and liquid phases. A consistent database including 567experimental data points is utilized in this study. In the first stepof modeling the parameters of the EoS have been tuned by regressionto the vapor pressure and saturated liquid density experimental datafor associating pure components (i.e., water and MDEA). In the nextstep, the vapor–liquid equilibrium calculations were carriedout for adjusting the molecular binary interaction parameters. Theseparameters are optimized for binary subsystems including H2O–CO2, CO2–MDEA, and H2O–MDEA. Finally, the binary interaction parameters betweenionic and molecular species are tuned via the simultaneous vapor–liquidand chemical reaction equilibrium calculations. The results show thatthe proposed model is able to improve the precision of predictionof the solubility of CO2in aqueous MDEA solution by morethan 6.7% in comparison with that of Huttenhuis et al. [FluidPhase Equilibria2008, 264,99–112] in a wide range of pressures, temperatures, acid gasloadings, and aqueous MDEA concentrations. [ABSTRACT FROM AUTHOR]

Abstract: Experimental measurements have been performed for liquid–liquid equilibria in aqueous systems containing 1-pentanol and sodium nitrate at temperatures of 298.15 and 308.15K and at atmospheric pressure. The results have been modeled using the extended UNIQUAC model and also a modified version of this model. Relevant model parameters have been adjusted using the experimental data. Both models are capable of correlating the experimental data with an average deviation of less than 0.8 weight percent, with the modified model producing slightly better results. The predictive nature of the models has also been verified. [Copyright &y& Elsevier]