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We have developed a global optimization program named PGA based on particle swarm optimization algorithm coupled with genetic operators for the structures of atomic clusters. The effectiveness and efficiency of the PGA program can be demonstrated by efficiently obtaining the tetrahedral Au20 and double‐ring tubular B20, and identifying the ground state ZrSi17–20− clusters through the comparison between the simulated and the experimental photoelectron spectra (PESs). Then, the PGA was applied to search for the global minimum structures of Mgn− (n = 3–30) clusters, new structures have been found for sizes n = 6, 7, 12, 14, and medium‐sized 21–30 were first determined. The high consistency between the simulated spectra and the experimental ones once again demonstrates the efficiency of the PGA program. Based on the ground‐state structures of these Mgn− (n = 3–30) clusters, their structural evolution and electronic properties were subsequently explored. The performance on Au20, B20, ZrSi17–20−, and Mgn− (n = 3–30) clusters indicates the promising potential of the PGA program for exploring the global minima of other clusters. The code is available for free upon request. [ABSTRACT FROM AUTHOR]

The presented work outlines a new genetic crossover operator, which can be used to solve problems by the Grammatical Evolution technique. This new operator intensively applies the one-point crossover procedure to randomly selected chromosomes with the aim of drastically reducing their fitness value. The new operator is applied to chromosomes selected randomly from the genetic population. This new operator was applied to two techniques from the recent literature that exploit Grammatical Evolution: artificial neural network construction and rule construction. In both case studies, an extensive set of classification problems and data-fitting problems were incorporated to estimate the effectiveness of the proposed genetic operator. The proposed operator significantly reduced both the classification error on the classification datasets and the feature learning error on the fitting datasets compared to other machine learning techniques and also to the original models before applying the new operator. [ABSTRACT FROM AUTHOR]

Passive Optical Networks (PONs) are telecommunication technologies that use fiber-optic cables to deliver high-speed internet and other communication services to end users. PONs split optical signals from a single fiber into multiple fibers, serving multiple homes or businesses without requiring active electronic components. PONs planning involves designing and optimizing the infrastructure for delivering fiber-optic communications to end users. The main contribution of this paper is the introduction of tailored operators within a genetic algorithm (GA) optimization approach for PONs planning. A three vector and an aggregator vector are devised to account, respectively, for physical and logical connections of the network, facilitating the execution of GA operators. This codification and these operators are versatile and can be applied to any population-based algorithm, not limited to GAs alone. Furthermore, the proposed operators are specifically designed to exploit the unique characteristics of PONs, thereby minimizing the occurrence of unfeasible solutions and accelerating convergence towards an optimal network design. By incorporating these specialized operators, this research aims to enhance the efficiency of PONs planning, ultimately leading to reduced costs and improved network performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees' fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance. [ABSTRACT FROM AUTHOR]

Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters.

A genetic algorithm (GA) is an evolutionary algorithm inspired by the natural selection and biological processes of reproduction of the fittest individual. GA is one of the most popular optimization algorithms that is currently employed in a wide range of real applications. Initially, the GA fills the population with random candidate solutions and develops the optimal solution from one generation to the next. The GA applies a set of genetic operators during the search process: selection, crossover, and mutation. This article aims to review and summarize the recent contributions to the GA research field. In addition, the definitions of the GA essential concepts are reviewed. Furthermore, the article surveys the real-life applications and roles of GA. Finally, future directions are provided to develop the field. [ABSTRACT FROM AUTHOR]

Passive Optical Networks (PONs) are telecommunication technologies that use fiber-optic cables to deliver high-speed internet and other communication services to end users. PONs split optical signals from a single fiber into multiple fibers, serving multiple homes or businesses without requiring active electronic components. PONs planning involves designing and optimizing the infrastructure for delivering fiber-optic communications to end users. The main contribution of this paper is the introduction of tailored operators within a genetic algorithm (GA) optimization approach for PONs planning. A three vector and an aggregator vector are devised to account, respectively, for physical and logical connections of the network, facilitating the execution of GA operators. This codification and these operators are versatile and can be applied to any population-based algorithm, not limited to GAs alone. Furthermore, the proposed operators are specifically designed to exploit the unique characteristics of PONs, thereby minimizing the occurrence of unfeasible solutions and accelerating convergence towards an optimal network design. By incorporating these specialized operators, this research aims to enhance the efficiency of PONs planning, ultimately leading to reduced costs and improved network performance.

In recent years, the global resource shortage has become a serious issue. Recycling end-of-life (EOL) products is conducive to resource reuse and circular economy and can mitigate the resource shortage issue. The disassembly of EOL products is the first step for resource reuse. Disassembly activities need tools, and tool deterioration occurs inevitably during the disassembly process. This work studies the influence of tool deterioration on disassembly efficiency. A disassembly line balancing model with the goal of maximizing disassembly profits is established, in which tool selection and assignment is a critical part. A modified discrete migratory bird optimizer is proposed to solve optimization problems. The well-known IBM CPLEX optimizer is used to verify the correctness of the model. Six real-world products are used for disassembly experiments. The popular fruit fly optimization algorithm, whale optimization algorithm and salp swarm algorithm are used for search performance comparison. The results show that the discrete migratory bird optimizer outperforms all three other algorithms in all disassembly instances. [ABSTRACT FROM AUTHOR]

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time. [ABSTRACT FROM AUTHOR]

Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing problem under the limitations of quantified penalties for hazardous component disassembly and the switching time between adjacent tasks. The objective function is to maximize the overall profit, which is equal to the disassembly revenue minus the total cost. A mixed-integer linear program is formulated to precisely describe and optimally solve the problem. Recognizing its NP-hard nature, a metaheuristic algorithm, inspired by human idea generation and population evolution processes, is devised to achieve near-optimal solutions. The exceptional performance of the proposed algorithm on practical test cases is demonstrated through a comprehensive comparison involving its solutions, exact solutions obtained using CPLEX to solve the proposed mixed-integer linear program, and those of competitive peer algorithms. It significantly outperforms its competitors and thus implies its great potential to be used in practice. As computing power increases, the effectiveness of the proposed methods is expected to increase further. [ABSTRACT FROM AUTHOR]

Forever and a day, breast cancer has caused significant negative impacts on the quality of lives of number of women, more often than not turning into a fatal disease. The growth in the number of such cases has constantly been a major concern for the community as well as medical experts. To prevent irreversible damages caused by the disease, early identification of breast cancer is essential. Various researches and techniques have been devised in the past as an attempt to achieve this task with appreciable accuracy. As an advancement to these pre-existing algorithms and methods, we have devised a model by exploiting the techniques of nature-inspired metaheuristics in order to efficiently detect breast cancer at an early stage while maintaining acceptable levels of accuracy. In this paper, we propose a hybrid model, namely "hybrid artificial bee colony and black hole with genetic operators (GBHABC)", for the early detection of breast cancer. In the proposed model, we employed a support vector machine (SVM) ensemble technique, optimized using the proposed GBHABC model. This model combines the techniques of two major algorithms, namely artificial bee colony (ABC) and black hole (BH), guided through crossover and mutation genetic operators. Datasets from the well-known UCI breast cancer repository have been used to train the models and evaluate test result. For a fair and accurate evaluation of the model, a number of metrics have been examined including accuracy, sensitivity, specificity, F1-score and precision. An impeccable accuracy of 99.42% was obtained on the UCI dataset, clearly outperforming any literature in the same field. [ABSTRACT FROM AUTHOR]

This study proposes a novel fuzzy programming optimization model for tugboat scheduling, directly considering multiple berthing bases, time windows, and operational uncertainties. The uncertainties in the required number of tugboats, the earliest start time, the latest start time, the processing time, and the start and end locations of each task are directly captured in the proposed fuzzy optimization model. The objective of the presented formulation is to minimize the total cost of fuel and delays. According to the characteristics of the problem, a Grey Wolf Optimization algorithm based on random probability encoding and custom genetic operators is proposed. The proposed algorithm, LINGO, the canonical Grey Wolf Optimization algorithm, and particle swarm optimization were used to compare and analyze the results of several examples. The results validate the efficiency of the proposed algorithm against the alternative exact and metaheuristics methods. Moreover, the findings from the conducted sensitivity analysis show the applicability of the developed fuzzy programming model for different confidence interval levels. [ABSTRACT FROM AUTHOR]

Abstract Background Various recent studies have investigated the use of traditional fibers (metallic or synthetic) as reinforcement in mortar. In recent times, there has been growing interest in using natural fibers as reinforcement in cement composites. This study was conducted to assess the impact of date palm, doum palm, and sisal fibers on the mechanical properties of cement composites. Genetic modeling was chosen to find the shear damage at the fiber-matrix interface of the three cement composites using genetic crossing operator, which allows us to calculate the damage at the interface using two damages of the matrix and the fibers, respectively. Results Our objective is to examine and evaluate the interface damage of date palm/mortar, doum palm/mortar and sisal/mortar under different mechanical tensile stresses ranging from 25 to 37 MPa with fiber volume fraction from 1 to 5%. It was found that the interface damage of date palm/mortar and doum palm/mortar cement composites was minimal compared to that of sisal/mortar. However, several researchers found that an increase in fiber volume fraction leads to decrease in mechanical properties and density in cement composites what we confirmed in this study that interface damage increases when the volume fraction increases. Conclusions The results are in line with the findings of a recent experimental study on the use of other plant fibers. Their results showed that incorporating ramie fibers resulted in a 27% increase in compressive strength, whereas the use of synthetic fibers resulted in 4% decrease in tensile strength in compression. It is recommended the use of doum and date palm natural fibers in the composition of mortars with a fiber volume fraction of 1 to 5% in order to reduce and avoid interface damage and limit the negative impact of synthetic fibers on the environment.

This paper proposes a new methaheurstic to solve the flow shop scheduling problem which is considered as an N P − hard problem for relatively high dimensions. The flow shop scheduling problems are commonly encountered in many industrial applications and manufacturing systems. For the purpose, a mixed integer linear programming model is presented to articulate the relationship between the objective function and the constraints of the problem. A proposed hybrid greedy algorithm based on the Nash equilibrium concept and the genetic operators is an attempt to outperform the classical algorithms frequently employed to approach the optimal solution of scheduling problems. In order to minimize the makespan criterion, various computational experiments were conducted for different size of the problem. Furthermore, a comparative study is performed to assess the developed methaheuristic against other algorithms. Simulations have shown that the proposed procedure is the most effective and robust. [ABSTRACT FROM AUTHOR]

We propose a hybrid system for supporting complex decisions through integrating neural networks (NNs) and genetic algorithms (GAs). We investigate the feasibility of leveraging the synergistic effect of integrating NNs and GAs to support stock market investment decisions. Utilizing 10-year daily US stock market data, we identified a set of effective attributes to predict stock market. The results suggest that our system is capable of exhibiting learning behavior and is a promising tool for stock market prediction. Though NNs have been successfully applied to a variety of pattern recognition applications, the connection weights generation process is highly computationally demanding. We apply GAs to search stochastically for connection weights. This alleviates an NN's lengthy training problem so that our hybrid system is more applicable to support complex decision making. Another contribution of our research is parameter setting for GAs. Parameter setting is a long-time thorny issue for GA implementation. We focus on one of the most difficult issues—the setting for the mutation rate. Using the stock market prediction as an application area, we have helped shed light on the role and importance of the mutation rate, as well as the complementary effect of mutation and crossover functions. Our findings favor "low-mutation-rates." [ABSTRACT FROM AUTHOR]

Image encryption research has seen massive advances in recent times, but many avenues of improvement still remain nascent. This paper takes head on various research challenges, giving the user fine grained control over their encryption requirements, by proposing a domain-flexible and selective image encryption scheme based on genetic algorithm, chaotic map, square-wave diffusion and orthogonal polynomials transformation. Initially, the proposed cryptosystem separates the image into important and unimportant regions making use of edges in the image with the orthogonal polynomials transformation. Important blocks, termed as Regions of Interest (ROI), are encrypted based on genetic operators and fitness score with chaos and unimportant blocks are encrypted with shuffling operations in the orthogonal polynomial domain. Then, square-wave diffusion is carried on the entire image to obtain the final encrypted image. The novel feature of the proposed encryption scheme is the unique design of the fitness function, wherein the fitness value can be varied between 1 for maximum speed and 10 for maximum security, to suit the user's requirements and can operate in frequency or spatial or hybrid domain suitable for a vast range of real-time applications. Extensive experiments and analyses have been conducted to demonstrate the efficiency of the proposed work. [ABSTRACT FROM AUTHOR]

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.

Resources scarcity, available capabilities and cost-benefit point of view, make it essential to select the best project(s) from available project portfolio. Project selection process has a significant role in the success. Here the main problem is what projects must be selected and how manage simultaneous projects. Used approach to answer these questions must be real, fast, global, flexible, economic and easy to use. It is clear that choosing a good approach for project selection problem with economic and non-economic criteria can be vital for a project manager to success within constraints. The complexity of this problem increases as the number of projects and the number of objectives increase. Therefore, in this research we aim to present a heuristic based on genetic and simulates annealing to select and prioritize available projects based on economic and non-economic criteria. Considered issues are benefit, credit and risk (technical and financial). Presented method starts from multi population of generated solutions and moves toward the final solution. Comparison studies between our method with other recently method in the literature demonstrates the capability of it to find a good basket of projects. Experimental results demonstrate that this method can be used for all kinds of projects basket.

Most of the Covering Salesman Problems (CSPs) addressed in the literature are considered full coverage. However, in real-life emergency situations like earthquake, flood, endemic and rural health care supply chain, full coverage of the cities may not always be possible due to various reasons like insufficient supply, limited time frame, insufficient manpower and damage of routes. In this study, we formulate a multi-objective CSP (MOCSP) restricting the number of nodes visited in a tour within a given range, so that a given percentage of nodes is at least covered. The objectives are maximization of coverage and minimization of tour length. Due to conflicting nature of the objectives, the problem is posed as a multi-objective optimization problem (MOOP). To solve the problem, the metaheuristic Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used with some modifications. The chromosome is designed to represent a tour with number of visited nodes within a given range. For the purpose of implementation, a one-dimensional array of variable length is used. New crossover and mutation operators are designed which are suitable for the problem and the corresponding chromosome representation. For simulation purpose, 19 benchmark test problems of Traveling Salesman Problem (TSP) from TSPLIB (Reinelt in ORSA J Comp 3:376–384) are used, where the number of nodes (i.e., cities) varies between 52 and 818. For each test problem, 12 instances are generated taking different values of problem parameters. Then, the set of optimal solutions are obtained for each instance, and the results are analyzed. A comparison of results for six test problems shows that our algorithm produces the best-known solutions for small and medium sized problems. However, for large sized problems, our algorithm produces better quality solutions in some cases only. [ABSTRACT FROM AUTHOR]

As a novel computing paradigm,edge computing has become a significant approach to solve large-scale scientific applications.Aiming at scientific workflow scheduling under edge environment,task computation time and data transmission time are uncertain due to the fluctuation of server processing performance and bandwidth,respectively.In order to help capture and reflect the uncertainty during workflow execution,task computation time and data transmission time are represented as triangular fuzzy numbers (TFN),based on fuzzy theory.Simultaneously,an adaptive discrete fuzzy GA-based particle swarm optimization (ADFGA-PSO) is proposed to minimize fuzzy execution cost of workflow while satisfying deadline constraint.Besides,two-point crossover operator,neighborhood mutation and adaptive multipoint mutation operator of genetic algorithm (GA) are introduced to avoid particles being trapped in local optimum.Experimental results show that,compared with others,scheduling strategy based on ADFGA-PSO can more effectively reduce fuzzy execution cost in regard to deadline-constrained scientific workflow scheduling under edge environment.

Neighborhood rough set is an excellent mathematical tool to carry out feature selection on both numerical and categorical dataset. However, conventional feature selection algorithms usually use greedy heuristic search strategies, which is easy to trap in the local extreme point. In this study, we propose a hybrid feature selection model that combines neighborhood rough set with an improved particle swarm optimization. The model computes the dependency degree of decision from neighborhood rough set as the objective function to evaluate the selected features, and then takes advantages of PSO’s stochastic search to discover the optimal solution more effectively. In order to improve the global search ability and alleviate the stagnation in local optima, the model modifies PSO part by adopting genetic operators and Levy flight. To evaluate the performance of this model, we implement experiments using twelve benchmark datasets and two classifiers ( $k$ NN and SVM). Compared with five representative filter-based approaches, experimental results show that our model can not only guarantee the stronger classification ability but also remove more redundant features in most datasets.

This study proposes a novel fuzzy programming optimization model for tugboat scheduling, directly considering multiple berthing bases, time windows, and operational uncertainties. The uncertainties in the required number of tugboats, the earliest start time, the latest start time, the processing time, and the start and end locations of each task are directly captured in the proposed fuzzy optimization model. The objective of the presented formulation is to minimize the total cost of fuel and delays. According to the characteristics of the problem, a Grey Wolf Optimization algorithm based on random probability encoding and custom genetic operators is proposed. The proposed algorithm, LINGO, the canonical Grey Wolf Optimization algorithm, and particle swarm optimization were used to compare and analyze the results of several examples. The results validate the efficiency of the proposed algorithm against the alternative exact and metaheuristics methods. Moreover, the findings from the conducted sensitivity analysis show the applicability of the developed fuzzy programming model for different confidence interval levels.

Resources scarcity, available capabilities and cost-benefit point of view, make it essential to select the best project(s) from available projects. Project selection process has a significant role in the success of investment. The main question is "what projects should be financed?" Applied approach to answer this, should be real, fast, global, flexible, economic and easy to use. It is clear that choosing a good approach for project selection problem with economic and non-economic criteria can be vital for a project manager to success within constraints. The complexity of the problem increases when the number of projects and the number of objectives increase. Therefore, in this research we aim to present a new heuristic method based on genetic and simulates annealing to select and rank available projects based on economic and non-economic criteria. Presented method starts from initial solutions including multi population generated solutions, and moves toward the final solution based on genetic operators and objective function. The proposed algorithm is evaluated on a set of randomly generated test problems with varying complexity. Comparison studies between our method with other recently method in the literature demonstrates the capability of it to find a good basket of projects. Experimental results prove that this method is applicable for all kinds of projects basket. [ABSTRACT FROM AUTHOR]

The sparrow search algorithm (SSA) tends to fall into local optima and to have insufficient stagnation when applied to the traveling salesman problem (TSP). To address this issue, we propose a novel greedy genetic sparrow search algorithm based on a sine and cosine search strategy (GGSC-SSA). First, the greedy algorithm is introduced to initialize the population and to increase the diversity of the population. Second, genetic operators are used to update the population, balancing global search and local development capabilities. Finally, the adaptive weight is introduced in the producer update to increase the adaptability of the algorithm and to optimize the quality of the solution, and a sin-cosine search strategy is introduced to update the scroungers. In addition, the GGSC-SSA is compared with the genetic algorithm (GA), simulated annealing (SA), particle swarm optimization (PSO), grey wolf optimization (GWO), ant colony optimization (ACO) and the artificial fish (AF) algorithm on TSP datasets for performance testing. We also compare it with some recently improved algorithms. The results of the simulations are encouraging; the GGSC-SSA significantly enhances the solution precision, optimization speed and robustness.

The application of Genetic Algorithm (GA) in solving any combinatorial problem presupposes the adoption of an encoding scheme and configuration of genetic operators that, according to the literature, impact the behavior of the GA population during the convergence phase. Understanding this behavior is essential to assist the refinement of configuration parameters and for proposing heuristics that support searching better quality solutions with the least possible computational effort. However, observing and understanding such behavior is not an easy task and, for this reason, this issue has attracted the attention of many researchers in recent years. In this work we proposed a computational tool and a method to evaluate the impact of different encoding schemes and settings for crossover and mutation operators in the GA performance. To this end, we have considered the application of GA in solving the Capacitated Vehicle Routing Problem (CVRP). However, it is important to highlight that the computational tool and the evaluation method are generalizable for the study of other population-based meta-heuristics and/or other combinatorial optimization problems. The results indicate that in most aspects binary encoding schemes are less efficient than schemes using integer numbers, and that the impact caused by genetic operators is directly related to the employed encoding scheme. It was also found that some of the performance measures proposed can be used either to propose heuristics or as heuristics itself. [ABSTRACT FROM AUTHOR]

This paper presents modified versions of a recent swarm intelligence algorithm called Harris hawks optimization (HHO) via incorporating genetic operators (crossover and mutation CM) boosted by two strategies of (opposition-based learning and random opposition-based learning) to provide perfect balance between intensification and diversification and to explore efficiently the search space in order to jump out local optima. Three modified versions of HHO termed as HHOCM, OBLHHOCM and ROBLHHOCM enhance the exploitation ability of solutions and improve the diversity of the population. The core exploratory and exploitative processes of the modified versions are adapted for selecting the most important molecular descriptors ensuring high classification accuracy. The Wilcoxon rank sum test is conducted to assess the performance of the HHOCM and ROBLHHOCM algorithms. Two common datasets of chemical information are used in the evaluation process of HHOCM variants, namely Monoamine Oxidase and QSAR Biodegradation datasets. Experimental results revealed that the three modified algorithms provide competitive and superior performance in terms of finding optimal subset of molecular descriptors and maximizing classification accuracy compared to several well-established swarm intelligence algorithms including the original HHO, grey wolf optimizer, salp swarm algorithm, dragonfly algorithm, ant lion optimizer, grasshopper optimization algorithm and whale optimization algorithm. [ABSTRACT FROM AUTHOR]

Featured Application: A memetic algorithm optimisation methodology for locating tables in indoor spaces during the COVID-19 pandemic. The COVID-19 pandemic has challenged all physical interactions. Social distancing, face masks and other rules have reshaped our way of living during the last year. The impact of these measures for indoor establishments, such as education or hostelry businesses, resulted in a considerable organisation problem. Achieving a table distribution inside these indoor spaces that fulfilled the distancing requirements while trying to allocate the maximum number of tables for enduring the pandemic has proved to be a considerable task for multiple establishments. This problem, defined as the Table Location Problem (TLP), is categorised as NP-Hard, thus a metaheuristic resolution is recommended. In our previous works, a Genetic Algorithm (GA) optimisation was proposed for optimising the table distribution in real classrooms. However, the proposed algorithm performed poorly for high obstacle density scenarios, especially when allocating a considerable number of tables due to the existing dependency between adjacent tables in the distance distribution. Therefore, in this paper, we introduce for the first time, to the authors' best knowledge, a Memetic Algorithm (MA) optimisation that improves the previously designed GA through the introduction of a Gradient Based Local Search. Multiple configurations have been analysed for a real hostelry-related scenario and a comparison between methodologies has been performed. Results show that the proposed MA optimisation obtained adequate solutions that the GA was unable to reach, demonstrating a superior convergence performance and an overall greater flexibility. The MA performance denoted its value not only from a COVID-19 distancing perspective but also as a flexible managing algorithm for daily table arrangement, thus fulfilling the main objectives of this paper. [ABSTRACT FROM AUTHOR]

This work presents a novel hybrid algorithm called GA-RRHC based on genetic algorithms (GAs) and a random-restart hill-climbing (RRHC) algorithm for the optimization of the flexible job shop scheduling problem (FJSSP) with high flexibility (where every operation can be completed by a high number of machines). In particular, different GA crossover and simple mutation operators are used with a cellular automata (CA)-inspired neighborhood to perform global search. This method is refined with a local search based on RRHC, making computational implementation easy. The novel point is obtained by applying the CA-type neighborhood and hybridizing the aforementioned two techniques in the GA-RRHC, which is simple to understand and implement. The GA-RRHC is tested by taking four banks of experiments widely used in the literature and comparing their results with six recent algorithms using relative percentage deviation (RPD) and Friedman tests. The experiments demonstrate that the GA-RRHC is a competitive method compared with other recent algorithms for instances of the FJSSP with high flexibility. The GA-RRHC was implemented in Matlab and is available on Github.

Featured Application: The obtainment of a methodology for maximizing the social distancing by increasing the distance among the school desks in the classrooms during the coronavirus pandemic through a Genetic Algorithm optimization. The COVID-19 pandemic has supposed a challenge for education. The school closures during the initial coronavirus outbreak for reducing the infections have promoted negative effects on children, such as the interruption of their normal social relationships or their necessary physical activity. Thus, most of the countries worldwide have considered as a priority the reopening of schools but imposing some rules for keeping safe places for the school lessons such as social distancing, wearing facemasks, hydroalcoholic gels or reducing the capacity in the indoor rooms. In Spain, the government has fixed a minimum distance of 1.5 m among the students' desks for preserving the social distancing and schools have followed orthogonal and triangular mesh patterns for achieving valid table dispositions that meet the requirements. However, these patterns may not attain the best results for maximizing the distances among the tables. Therefore, in this paper, we introduce for the first time in the authors' best knowledge a Genetic Algorithm (GA) for optimizing the disposition of the tables at schools during the coronavirus pandemic. We apply this GA in two real-application scenarios in which we find table dispositions that increase the distances among the tables by 19.33% and 10%, respectively, with regards to regular government patterns in these classrooms, thus fulfilling the main objectives of the paper. [ABSTRACT FROM AUTHOR]

The particle filter (PF) perform the nonlinear estimation and have received much attention from many engineering fields over the past decade. However, the standard PF is inconsistent over time due to the loss of particle diversity caused mainly by the particle depletion in resampling step and incorrect a priori knowledge of process and measurement noise. To overcome these problems, intelligent adaptive unscented particle filter (IAUPF) is proposed in this paper. The IAUPF uses an adaptive unscented Kalman filter filter to generate the proposal distribution, in which the covariance of the measurement and process of the state are online adjusted by predicted residual as adaptive factor based on a covariance matching technique. In addition, it uses the genetic operators to increase diversity of particles. Three experiment examples show that IAUPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. The effectiveness of IAUPF is demonstrated through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper is the result of a research project developed by the DIGITI’s research group at Francisco José de Caldas University, on optimization problems by using artificial intelligence and it shows the implementation of a genetic algorithm (GA) as a tool for planning and optimization transport tours, with the goal of finding the best path destinations for a fleet of vehicles. It presents basic concepts of the theory and the results obtained, about the administration and logistics in the supply chain, through a planning solution that optimizes the use of transportation resources.

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