Search

Network function virtualization (NFV) is a contemporary network architecture concept that integrates complex network functions into software-based virtual network functions (VNFs) instead of specialized hardware. Resource allocation for multiple service function chain requests (SFC) in NFV-enabled networks is a fundamental challenge for network providers. Several research studies have focused on this problem in detail. Specifically, SFC routing attempts to satisfy each request by assigning a path across data centers that places the ordered set of required VNFs. This paper formulates this problem as an SFC provisioning problem (SFCRP) with the two objectives of maximizing the number of accepted requests and balancing the utilization of network resources. To address this problem, a two-stage routing algorithm (2PRT) that integrates Yen's algorithm and a genetic algorithm is proposed. The simulation results based on 12 scenarios that use three real-world networks and randomly generated requests indicate that the batch mode significantly enhances the acceptance rate compared to the existing methods. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the laser cladding process parameters of WC/Inconel 625 composite on a low-carbon steel substrate. This process aims to improve industrial parts' wear and corrosion properties in corrosive and high-temperature environments. Laser power, laser cladding speed, powder-feeding rate, and WC content percentage were process input parameters, each at three levels. The dilution ratio and micro-hardness of samples were considered as process responses. Taguchi orthogonal arrays were employed for experimental design, and the results were analyzed using signal-to-noise ratio and analysis of variance. A linear regression model was developed to understand the relationship between process parameters and responses. In addition, two-objective optimization of the process parameters using the second-generation non-dominated sorting genetic algorithm (NSGA-II), and a hybrid algorithm based on the multi-objective whale optimization algorithm (MOWOAGA) were carried out to determine the optimal parameters that minimize the dilution ratio and maximize the cladding hardness. The results showed that the laser power and the powder-feeding rate have the most excellent effect on the dilution ratio. In contrast, WC content percentage and powder-feeding rate strongly affect micro-hardness. Based on the applied meta-heuristic algorithms and confirmed by experimental tests, the laser power of 370 W, laser cladding speed of 4 mm/s, powder-feeding rate of 8 g/min, and WC content percentage of 20.5% are the most optimal process parameters that lead to micro-hardness of 397 Vickers and dilution percentage of 2.5%. This research guides selecting, controlling, and optimizing WC/Inconel 625 composite laser cladding process parameters. [ABSTRACT FROM AUTHOR]

The index tracking problem consists of constructing a portfolio that has the closest performance of a given index as possible, with fewer assets in its composition. When considering a large number of assets, including all of them on the solution may incur high transaction fees, harming the accumulated returns in the long term. The constraint that limits the portfolio size imposes a combinatorial nature on the problem and the computation of the optimal solution becomes infeasible as the universe of assets grows. To get around this issue, pure and hybrid metaheuristics have been proposed in the literature to achieve good solutions in practical time. Different from pure metaheuristics, hybrid metaheuristics do not need any constraint handling or solution repairing approaches since they often use general-purpose solvers to adjust portfolio weights. This work presents a comparison between pure and hybrid genetic algorithms, which is one of the most popular heuristics applied in the portfolio selection field. We considered linear and nonlinear index tracking models in the experiments, where the GA that obtained the best performance in a single period optimization strategy, was selected to backtest in a dynamic index tracking approach. The results showed that hybrid GAs can compute good or even better solutions than the CPLEX solver and pure GAs, in a shorter time. [ABSTRACT FROM AUTHOR]

The Whale Optimisation Algorithm (WOA) is a meta-heuristic model inspired by the hunting behaviours of humpback whales. Similar to many other meta-heuristic models, e.g., Particle Swarm Optimisation (PSO), Artificial Bee Colony (ABC), and Ant Colony Optimisation (ACO), the WOA is susceptible to the issues of slow convergence and local optima. In this study, we address these short comings by first proposing an Enhanced WOA (EWOA) model for tackling single-objective optimisation. Specifically, EWOA integrates the WOA and Differential Evolution (DE). DE is a population-based algorithm that generates new candidate solutions by combining the existing ones, employing a simple yet robust formula. This amalgamation aids in generating diverse solutions during the exploration stage by utilising a non-linear coefficient vector, adaptive weight, and sub-population strategies. Furthermore, fast non-dominated sorting and crowding distance techniques from the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are incorporated into EWOA, resulting in a multi-objective EWOA (MOEWOA) model. We evaluate both EWOA and MOEWOA with a broad spectrum of benchmark functions. The results from 51 single-objective optimisation problems indicate the usefulness of EWOA in terms of a fast convergence rate and with increased performance. On the other hand, MOEWOA demonstrates a better convergence rate and an effective balance between convergence and diversity in 12 multi-objective optimisation problems. In addition, MOEWOA successfully solves 21 complex multi-objective constrained mechanical design problems, outperforming other compared algorithms at the 95% confidence level. The empirical outcomes of our study indicate the potential of EWOA and MOEWOA for undertaking complex, real-world optimisation problems. [ABSTRACT FROM AUTHOR]

In the real market, the item's demand is substantially affected by the item's selling price and frequency of advertising. This study focuses on an optimal ordering policy followed by advertising, pricing, and preservation policies. The present study incorporates the quantity-dependent ordering cost and time-dependent holding cost. A partial prepayment scheme is developed for inventory purchase decisions. The spoilage impact can be effectively reduced by an optimal investment in refrigeration. The optimal decision policy has been proposed by using three metaheuristic algorithms, namely particle swarm optimization, real-coded genetic algorithm, and differential evolution algorithm. A comparison is made for these metaheuristic schemes based on the numerical illustrations. The parameter sensitivity is performed to get insights of the variability in the indicators of the inventory model. [ABSTRACT FROM AUTHOR]

This study proposes a hybrid multi-objective meta-heuristics and possibilistic intuitionistic fuzzy c-means (PIFCM) algorithms for cluster analysis. The PIFCM algorithms combine Atanassov's intuitionistic fuzzy sets (IFSs) with possibilistic fuzzy c-means (PFCM) algorithms. In this study, three metaheuristic algorithms are used to improve the clustering results, specifically a genetic algorithm (GA), a particle swarm optimization (PSO) algorithm, and a gradient evolution (GE) algorithm. Therefore, three clustering algorithms are proposed, including multi-objective GA-based PIFCM (MOGA–PIFCM), multi-objective PSO-based PIFCM (MOPSO–PIFCM), and multi-objective GE-based PIFCM (MOGE–PIFCM). Their performance results are compared with those of other clustering algorithms, such as intuitionistic fuzzy c-means (IFCM) algorithm, possibilistic intuitionistic fuzzy c-means (PIFCM) algorithm, single-objective GA–PIFCM algorithm, single-objective PSO–PIFCM algorithm, and single-objective GE–PIFCM algorithm using 15 benchmark datasets. Adjusted Rand Index and accuracy measures are employed as performance indices for comparison. Experiment results shows that the MOGE–PIFCM obtains better solutions than the other clustering algorithms in terms of all performance validation indices. [ABSTRACT FROM AUTHOR]

This paper addresses an integrated material flow optimization problem of cross-docking satellites, in which the transportation problem, the truck-door assignment problem with material placement plans, and the two-dimensional truck loading problem are taken into account. The study aims to find the best distribution and operational plans for the cross-docking satellites to minimize the total transportation cost of the materials. To solve the considered problem, a hybrid genetic algorithm (HGA) is developed, which integrates simulated annealing (SA) algorithm within a genetic algorithm (GA). In this way, a new individual with a low solution quality is rejected by using the stochastic solution acceptance feature of the SA. Moreover, the HGA is enhanced with an advanced two-dimensional loading-check procedure and a rule-based material placement procedure to obtain efficient solutions. The proposed loading-check procedure reduces the processing time of the HGA by avoiding duplicate examinations for the truck loading plans. Likewise, the rule-based material placement procedure prevents unnecessary searches for the assignment plans of the products in a temporary storage area. In computational studies, the performance of the HGA is tested on two different problem sets by comparing HGA with the SA and GA. Furthermore, the HGA is applied to a problem set that is formed by using real-life data of a logistics company. The computational results show that the HGA introduces effective solutions and outperforms both the SA and GA. In addition, the results of the real-life application denote that the HGA can be employed to find effective material flow plans in real situations of cross-docking operations. [ABSTRACT FROM AUTHOR]

This study explores the applicability of the metaheuristic algorithm for calculating the building period. Firefly algorithm (FA) employing the feed-forward (FF) model is applied to an existing period dataset of several wood buildings, whose motions were recorded at ambient conditions. The composition of the considered artificial neural network (ANN) is optimized employing the firefly algorithm, and the fixed weights are determined with the least error for the model. The model's precision is evaluated by comparing the results with the multiple linear regression (MLR) model, the ANN model combined with the genetic algorithm (GA-ANN), particle swarm optimization algorithm (PSO-ANN), and the models existing in building code. The results are also compared with the models presented in the literature for the period estimate required for the seismic design of wood buildings. The paper concludes that the proposed informational model can predict the fundamental period of light-frame wood buildings more accurately than the other available models. [ABSTRACT FROM AUTHOR]

Meta-heuristics have been successfully applied to many complex optimization problems. One of the main reasons for its success is its ability to handle non-convex, nonlinear, multimodal, multi-variable, and multi-objective problems with easy implementation. However, the quality of the response of these algorithms to an optimization problem is highly susceptible to the control parameters, and few works aim to tune them or find tools that can improve the algorithms. The literature is rich in proposals for new algorithms, but not for improving existing ones. This paper presents different strategies for tuning and accelerating meta-heuristics using the first hybrid algorithm in the literature. The Lichtenberg algorithm is inspired by lightning and Lichtenberg figures and has been increasingly successfully applied to various optimization problems. However, a study of its best parameters has never been presented until now. After a discussion of the best tuning tools, its tuning parameters are performed using response surface methodology. Then, 14 versions are studied through 10 test functions using chaos theory and Lévy flights scenarios. After 13,500 simulations, the chaotic Lichtenberg algorithm equipped with the piecewise function and tuned parameters proved the best version with only 16% similarity to the original algorithm. Then, it was compared to the genetic algorithm, particle swarm optimization, gray wolf optimizer, salp swarm optimization, whale optimization algorithm, and dragonfly algorithm. The proposed algorithm had both the best average accuracy, lower computational cost, and the smallest standard deviation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop and test three meta-heuristic algorithms to solve large size multi-objective supply chain network design problems. The algorithms are based on tabu search, genetic algorithm, and simulated annealing to find near-optimal global solutions. The three algorithms are designed, coded, and tested, and their parameters are fine tuned. The exact ε-constraint algorithm embedded in the General Algebraic Modeling System is used to validate the results of the three algorithms. The algorithms are compared using a typical multi-objective supply chain model utilizing several performance measures. The measures include the mean ideal distance, diversification metric, and percent of domination, inverted generational distance, and computation time. The results show that the tabu search algorithm outperformed the other two algorithms in terms of the percent of domination and computation time. On the other hand, the simulated annealing solutions are the best in terms of their diversity. The work in this paper is expected to help managers to solve large-scale supply chain problems that arise in oil and gas, petrochemical, and food supply chains. [ABSTRACT FROM AUTHOR]

Estimating market demand for a product is essential to business growth; yet, it is exceedingly difficult. This article considers various demand patterns for a single product in a three-echelon supply chain where a single supplier, a single manufacturer and a single retailer are involved. A robust inspection process is considered at the manufacturer's end to deliver a high-quality product to the retailer. The backorder strategy is incorporated to enhance the company's trust and reputation among consumers. The major idea of this research is to analyze the nature of the product's demand in three distinct situations, as well as the benefits of implementing the backorder technique. Three cases are analyzed on the basis of different demand patterns, in which the demand rate is assumed to be constant (i.e., deterministic), price-dependent and fuzzy in the first, second and third cases, respectively. To verify this model, numerical examples are tested independently with identical information for each case and owing to the complexity of the objective function; a genetic algorithm is employed to obtain the best solution. Finally, sensitivity analysis, managerial insights and conclusions, including the future directions, are provided. [ABSTRACT FROM AUTHOR]

Diabetes is a set of long-term metabolic issues characterized by high blood glucose levels over a drawn-out time. It is a challenging and threatening disease because nearly half of patients who have diabetes do not know that they have it. Data mining techniques have been heavily utilized to predict diabetes in its earliest stage, and therefore can potentially reduce medication costs. Most of the existing research used metaheuristics to optimize neural network parameters or used other data mining algorithms to classify diabetes. This research integrates design of experiments (DOE), genetic algorithm (GA), and multilayer perceptron (MLP) to classify diabetic patients. Two sets of factorial designs were used to determine an optimal set of parameters for the GA. GA is used to optimize the parameters in the MLP, where the MLP acts as a simulator within the GA to provide the fitness evaluation of the solutions. The best resulting solution from DOE–GA–MLP represents the optimized MLP that achieves the highest classification accuracy in diabetes patients. The proposed approach outperforms eight different classification algorithms in terms of accuracy, sensitivity, and F1-score. The developed DOE–GA–MLP model presents a robust predictive tool that can help healthcare professionals identify patients with diabetes in its early stage. Intervention and/or preventive strategies can be implemented in those identified cases to improve patient care and possibly reduce diabetes rates and potential expenditures. [ABSTRACT FROM AUTHOR]

Hierarchical Single-Allocation Hub Median Facility Location Problem (SA-H-MP) is a well-known optimization problem having single objective function. The objective of SA-H-MP is to find a network of demand nodes, hubs and central hubs so that the service cost is minimized. In this article, we have proposed two meta-heuristic algorithms, Genetic Algorithm (GA) and its refined version and compare their performances with CPLEX (exact method), Simulated Annealing (SA) and Iterated Local Search (ILS). These approaches are validated on the well-known benchmark CAB data set. Proposed refined version of the GA is found to improve the result over other meta heuristics in more than 88% cases. [ABSTRACT FROM AUTHOR]

A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier's prediction errors and allows for faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in the onlooker bee phase (exploitation phase) of ABC to boost performance by maintaining the balance between exploration and exploitation of ABC. Tuned the modified ABC algorithm by using Naïve Bayes (NB) classifiers to improve the further accuracy of the model. Independent Component Analysis (ICA) is used for dimensionality reduction. In the first step, the reduced dataset is optimized by using Modified ABC and after that, in the second step, the optimized dataset is used to train the NB classifier. Extensive experiments were performed for comprehensive comparative analysis of the proposed algorithm with well-known metaheuristic algorithms, namely Genetic Algorithm (GA) when used with the same framework for the classification of six high-dimensional cancer datasets. The comparison results showed that the proposed model with the CS algorithm achieves the highest performance as maximum classification accuracy with less count of selected genes. This shows the effectiveness of the proposed algorithm which is validated using ANOVA for cancer classification. [ABSTRACT FROM AUTHOR]

Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems including several models and nonlinearity features. Additionally, it offers a comprehensive solution for optimizing parameters in various field-road segmentation models. [ABSTRACT FROM AUTHOR]

Encryption of a plaintext involves a secret key. The secret key of classical cryptosystems can be successfully determined by utilizing metaheuristic techniques. Monoalphabetic cryptosystem is one of the famous classical cryptosystems. In this paper, we determine the secret key of the monoalphabetic cryptosystem using a recently proposed metaheuristic technique, namely, red deer algorithm. The existing red deer algorithm framework has been tailored to solve the above considered problem. Performance of the developed red deer algorithm is compared with the following metaheuristic techniques: tabu search, genetic algorithm, and cuckoo search using three criteria, namely, effectiveness, efficiency, and accuracy. The results obtained show that the proposed red deer algorithm can compete with all the above three algorithms with respect to all the criteria. This signifies the importance of the proposed red deer algorithm is that it can be utilized to solve the similar problems effectively, efficiently, and with more accuracy. [ABSTRACT FROM AUTHOR]

Molecular biology advances in the past few decades have contributed to the rapid increase in genome sequencing of various organisms; sequence alignment is usually considered the first step in understanding a sequence's molecular function. Understanding such a function is made possible by aligning an unknown sequence with known sequences based on evolution. An optimal alignment adjusts two or more sequences in a way that it could compare the maximum number of identical or similar residues. The two sequence alignments types are Pairwise Sequence Alignment (PSA) and Multiple Sequence Alignment (MSA). The MSA enjoys a higher advantage than PSA since it predicts the similarity in a family of similar sequences, providing more biological data. Moreover, while the dynamic programming (DP) technique is used in PSA to provide the optimal method, it will lead to more complexity if used in MSA. So, the MSA mainly uses heuristic and approximation methods. Such methods include progressive alignment, iterative alignment, Hidden Markov Model (HMM), and Metaheuristic algorithms. This paper presents a genetic algorithm and chromosome design to solve a mathematical model for MSA. This model uses a basis for an optimal solution in different ways, and an X-mediated matrix with binary elements is used to model the sequence. Then, the model is implemented using the Genetic Algorithm method on the web, and the final results indicate the success of GA for the MSA approach. [ABSTRACT FROM AUTHOR]

In this paper, a new U-shaped assembly line balancing problem is studied. For the first time, the criteria such as equipment cost, number of stations and activity performing quality level are considered to be optimized simultaneously by activity to station and worker to station decisions. For this aim, a multi-objective nonlinear formulation is proposed and its linearized version is also presented. Since, according to the literature, the U-shaped assembly line balancing problem with equipment requirements is an NP-hard problem, the problem of this study is NP-hard too. Because of this complexity, the classical algorithms like simulated annealing, variable neighborhood search, and classical genetic algorithm with a novel encoding/decoding scheme are used as solution approaches. As an extension, two hybrid versions of the proposed classical algorithms are proposed according to the characteristics of the problem. In order to evaluate the proposed meta-heuristics, because the problem is new, some test problems are generated randomly. Computational study of the paper, including sensitivity analysis of the proposed meta-heuristics and final experiments on the test problems, proves the superiority of the hybrid versions of the classical algorithms. [ABSTRACT FROM AUTHOR]

Researchers from different domains have developed several metaheuristic algorithms that are inspired by the biological phenomenon. In this work, we propose a novel algorithm that is inspired by the prey–predator interaction of animals. In the proposed algorithm, two random populations are considered as a predator and preys. The energy gain for both predators and preys calculated based on their body mass and the interaction between predators and their mutual preys. The best predator (i.e., predator with the highest energy gain) performs a local search (exploitation). The other solution members in prey population facilitate the exploration of the search space. The proposed algorithm is evaluated by three phases. Firstly, a set of 16 mathematical functions are considered to test different characteristics of predator–prey optimization (PPO) algorithm as a optimizer. Secondly, it is evaluated by seven datasets as a solver for feature selection problem. The proposed algorithm as an global optimizer has been compared with other metaheuristic algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), ant lion optimizer (ALO), gray wolf optimizer (GWO), whale optimization algorithm (WOA), differential evolution (DE), sine–cosine algorithm (SCA), moth flame optimization (MFO), and salp swarm algorithm (SSA). The results indicate that PPO improves convergence rate by 33.8%, 24.2%, 37.3%, 10.5%, 12.4%, 22.7%, 18.6%, 30.7%, and 29.1% in average compared to GA, PSO, ALO, GWO, WOA, DE, SCA, MFO, and SSA, respectively. Furthermore, PPO obtains a higher Performance Index around 5–10% than competitor approaches with respect to exploration–exploitation balance and a high degree of stability. Secondly, the performance of PPO for feature selection issues demonstrates the applicability PPO algorithm in solving real problems with unknown search spaces. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel hybrid metaheuristic for the Knapsack Problem with Forfeits (KPF). KPF is a recently introduced generalization of the Knapsack Problem. In this variant, a penalty cost incurs whenever both items composing a so-called forfeit pair belong to the solution. Our proposed algorithm, called GA–CG Forfeits, combines the strengths of the Genetic and Carousel Greedy paradigms. In this work, we define the algorithm and compare it with two previously proposed heuristics on a set of benchmark instances. In these tests, GA–CG Forfeits provided significantly better solutions than the other two algorithms on all instances. [ABSTRACT FROM AUTHOR]

Handwritten signature verification is one of the most fast, intuitive and cost-effective tools for user authentication. However, the analysis of the handwritten signature may become complicated due to the large dimensionality. Feature selection is treated as one of the most commonly applied preprocessing techniques in data mining to deal with the dimensionality. Since the feature selection can be considered as an optimization problem, we propose a wrapper metaheuristic framework for the handwritten signature verification, which uses the k-nearest neighbor classification algorithm to guide a metaheuristic optimization algorithm in order to find the best subset of features. The following metaheuristics are used in the work: swallow swarm optimization (SSO), binary dragonfly swarm optimization (BBA), population algorithm with memory (PAO), genetic algorithm (GA), binary bat algorithm (BBA), binary whale optimization algorithm (WOA) and particle swarm optimization (PSO). The experiments are carried on the SVC2004 dataset by comparing the employed metaheuristics with each other from different perspectives, including the classification performance, the relatives frequencies of selected features and the feature subset size. According to results, the proposed wrapper framework obtains extremely promising results for handwritten signature verification. In particular, the proposed framework can increase the classification accuracy at least or more than 8%, even though selecting between 2 and 6 features among 100 features in most cases. It is also observed that the proposed framework with PSO can achieve slightly better results than with the other metaheuristics in most cases. It can therefore be suggested that the traditional metaheuristics like PSO and GA maintain their position and entrench their popularity in a wide range of fields despite a variety of the recently introduced metaheuristics. [ABSTRACT FROM AUTHOR]

Today, there are a lot of useful algorithms for covering array (CA) generation, one of the branches of combinatorial testing. The major CA challenge is the generation of an array with the minimum number of test cases (efficiency) in an appropriate run-time (performance), for large systems. CA generation strategies are classified into several categories: computational and meta-heuristic, to name the most important ones. Generally, computational strategies have high performance and yield poor results in terms of efficiency, in contrast, meta-heuristic strategies have good efficiency and lower performance. Among the strategies available, some are efficient strategies but suffer from low performance; conversely, some others have good performance, but is not such efficient. In general, there is not a strategy that enjoys both above-mentioned metrics. In this paper, it is tried to combine the genetic algorithm and the Augmented Lagrangian Particle Swarm Optimization with Fractional Order Velocity to produce the appropriate test suite in terms of efficiency and performance. Also, a simple and effective minimizing function is employed to increase efficiency. The evaluation results show that the proposed strategy outperforms the existing approaches in terms of both efficiency and performance. [ABSTRACT FROM AUTHOR]

In this paper, a Joint set-up procedure for tuning metaheuristic algorithms' parameters is proposed. The approach is applied to a genetic algorithm (GA) and tested further on the artificial bee colony (ABC) algorithm. The joint influence of parameters (the crossover and mutation probabilities for GA and the number of population and limit for ABC) on the performance of the algorithms is investigated. As a case study, a model parameter identification of an E. coli fed-batch cultivation process is considered. E. coli is one of the most commonly used bacteria for producing medical substances in the pharmaceutical industry. The development of an effective model of a fed-batch cultivation process is very important. The processes in a bioreactor are usually described by a system of parametric nonlinear differential equations. The model parameter identification is a difficult optimization problem, which cannot be solved by applying traditional numerical methods. Feasibilities of GA and ABC for a model parameter identification of a nonlinear fed-batch cultivation process based on real experimental data are presented. The application of the proposed Joint set-up approach leads to a significant improvement in the performance of GA and ABC. As a result, a reasonable enhancement of the E. coli cultivation model accuracy is achieved. The main advantage of the tuning procedure, which searches an optimal set of values of GA and ABC control parameters, focusing on promising intervals of variation of the parameter values and refining their ranges, is that the computational efforts are reduced by more than 60% for the ABC algorithm and more than 90% for GA. [ABSTRACT FROM AUTHOR]

Hazardous wastes' volume produced by human activities has increased in recent years. Consequently, associated risks involved in the treatment, recycling, disposing, and transportation of these hazardous materials have become more attractive for the researchers. In this study, we propose a new model for hazardous waste location routing problem. Appending the service time window and workload balance to the previous mathematical models can be taken into account as the major contributions of this study. Three objective functions including two systematic goals (cost and risk) and one social goal (workload balancing) have been considered for the model. Compatibility between wastes and a heterogeneous fleet of vehicles, which are rarely investigated in the literature, is discussed in this paper. Since the proposed model is classified as a multi-objective model, three multi-objective evolutionary algorithms, namely Non-dominated Sorting Genetic Algorithm II (NSGA-II), Pareto Envelope-based Selection Algorithm II (PESA-II), and Strength Pareto Evolutionary Algorithm II (SPEA-II) are employed. As two other innovations, an adaptive penalty function is developed and the PESA-II is modified by removing replicated solutions from its archive and their obtained results are discussed. Finally, by experimenting a number of test problems in different sizes, it is demonstrated that proposed modified PESA-II and SPEA-II perform better than NSGA-II in most of comparison metrics including feasible answers exploration, CPU time, spacing metric, inverted generational distance, quality metric, etc., whereas, NSGA-II creates more spread Pareto frontiers which are suitable for decision-maker to choose, from among a range of different options. [ABSTRACT FROM AUTHOR]

Cloud computing delivers practical solutions for long-term image archiving systems. Cloud data centers consume enormous amounts of electrical energy that increases their operational costs. This shows the importance of investing on energy consumption techniques. Dynamic placement of virtual machines to appropriate physical nodes using metaheuristic algorithms is among the methods of reducing energy consumption. In metaheuristic algorithms, there should be a balance between both exploration and exploitation aspects so that they can find better solutions in a search space. Exploration means looking for a solution in a wider area, while exploitation is producing new solutions from existence ones. Artificial bee colony optimization, which is a biological metaheuristic algorithm, is a sign-oriented approach. It has a strong exploration ability, but a relatively weaker exploitation power. On the other hand, tabu search is a popular algorithm that shows better exploitation in comparison with ABC. In this study, cloud computing environments are detailed with an allocation protocol for efficient energy and resource management. The technique of energy-aware allocation splits data centers (DCs) resources among client applications end routes to enhance energy efficacy of DCs and also achieves anticipated quality of service (QoS) for everyone. Heuristic protocols are exercised for optimizing the distribution of resources to upgrade the efficiency of DC. In the current paper, energy-aware resources allotment technique is employed and optimized in clouds via a new approach called Tabu Job Master (JM). Tabu JM claims the benefits of some variables and also rapid convergence speeds. Results are duly achieved for energy consumption—the count of virtual machines (VMs) migration and also makespan. The results shown by Tabu JM are benchmarked by using genetic algorithm (GA), artificial bee colony (ABC), ABC with crossover and technique of mutation, the basic tabu search techniques, and Tabu Job Master. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid method for training high-order Takagi–Sugeno fuzzy systems using sparse regressions and metaheuristic optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of fuzzy rules. The fuzzy sets can be chosen manually or determined by a metaheuristic optimization method (particle swarm optimization, genetic algorithm or simulated annealing), while the polynomials are obtained using ordinary least squares, ridge regression or sparse regressions (forward selection, least angle regression, least absolute shrinkage and selection operator, and elastic net regression). A quality criterion is proposed that expresses a compromise between the prediction ability of the fuzzy model and its sparsity. The conducted experiments showed that: (a) the use of sparse regressions and/or metaheuristic optimization can reduce the validation error compared with the reference method, and (b) the use of sparse regressions may simplify the fuzzy model by zeroing some of the coefficients. [ABSTRACT FROM AUTHOR]

Intelligent video analytics for pedestrian detection plays a vital role for enhanced and effective surveillance system. Since smart city projects are gaining momentum in most of the countries nowadays, enhanced pedestrian detection plays a vital role in the field of security and surveillance. Various classification models were in existence for detecting the pedestrians which suffers from variety of challenges like illumination, pedestrian outfits, gestures, occlusion, lighting, etc., that affects the accuracy of detection. A strong feature vector describing the pedestrian is developed to enhance the accuracy of detection. In this paper, a novel hybrid metaheuristic pedestrian detection (HMPD) approach is proposed to enhance the accuracy of the classifier. HMPD extracts the working principles of support vector machine and genetic algorithm. The proposed model is trained using a set of human and non-human images. The accuracy of the proposed model is tested with benchmarking video data available at VISOR repository. The result clearly shows that HMPD approach produces the maximum accuracy than any traditional approaches. HMPD approach can further be applied in other domains for enhanced security and surveillance. [ABSTRACT FROM AUTHOR]

In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design optimization. Moreover, four recent variations of evolutionary-based algorithms [i.e., improved differential evolution algorithm based on an adaptive mutation scheme, weighted differential evolution algorithm (WDE), linear population size reduction success-history-based adaptive differential evolution algorithm, and biogeography-based optimization with covariance matrix-based migration] have been tackled for handling the current problem. The objective function is based on the cost of shallow foundation designs that satisfy ACI 318-05 requirements is formulated as the objective function. This study addresses shallow footing optimization with two attitudes, routine optimization, and sensitivity analysis. As a further study, the effect of the location of the column at the top of the foundation is examined by adding two additional design variables. Three numerical case studies are used for both routine and sensitivity analysis. Moreover, the most common evolutionary-based technique, genetic algorithm (GA), is considered as a benchmark to evaluate the proposed methods' efficiency. Based on the results, there is no algorithm which works as the most efficient solver over all the cases; while, BBO and WDE showed an acceptable performance because of satisfying records in most cases. There were several cases in which GA, DE, and ES were incapable of finding a valid solution which meets all the constraints simultaneously. [ABSTRACT FROM AUTHOR]

Nature-inspired heuristic and/or metaheuristic algorithms have been used for solving complex real-world problems in recent years. Electrohydrodynamic (EHD) inkjet printing is a microadditive manufacturing process in which high-resolution jets of polarizable functional materials were deposited on the defined spot of a substrate at the appointed time. The quality of the printed features is derived by the complex physics of the system. Parameter modeling of this process was carried out by using regression analysis, a feed-forward neural network trained with backpropagation (BPNN) and a neural network trained with a genetic algorithm (GA-NN) separately. This study emphasizes the droplet diameter prediction of an EHD inkjet printing system and explores the applicability of the soft computing-based methods for this new emerging technology. Soft computing-based approaches have been developed for the first time in this area to model the EHD inkjet process. Five hundred data were produced through the conventional regression analysis to train the neural network-based models. Output droplet diameter was predicted for different combinations of input parameters such as standoff height (SH), applied voltage (AV) and ink flow rate (FR) using the above three approaches, and their performances were analyzed through some randomly created real experimental test cases. All three models gave good prediction accuracy with less than 10% error in the prediction of the droplet diameter. Furthermore, it had been observed that the performance of GA-NN surpasses both the regression- and BPNN-based approaches in most of the test cases. It achieved quite satisfactory average absolute percentage deviation value of 2.51% between the target and predicted output using GA-NN model, which also showed an improvement over the regression or BPNN model. [ABSTRACT FROM AUTHOR]

This paper deals with hybrid flow shop scheduling problem with unrelated and eligible machines along with fuzzy processing times and fuzzy due dates. The objective is to minimize a linear combination of total completion time and maximum lateness of jobs. A mixed integer mathematical model is presented for the problem. The most challenging parts of hybrid evolutionary algorithms are determination of efficient strategies by which the whole search space is explored to perform local search around the promising search areas. In this study, a clustering-based approach as a data mining tool is introduced to identify the promising search areas. A repetitive clustering with an evolutionary algorithm is simultaneously employed to determine more promising parts of the solution space. Then, the searches in those parts are intensified with a local search. Here, two clustering-based meta-heuristic algorithms are applied to solve the problem, namely particle swarm optimization and genetic algorithm. The parameters are tuned by Taguchi experimental design, and various randomly generated test problems are used to evaluate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Up to now, many different methods (e.g., dispatching rules, metaheuristics) have been proposed to solve the job shop scheduling problem (JSSP), but the application of data mining approaches in the literature is limited. In this paper, we propose a data mining-based approach to generate an improved initial population for population-based heuristics/metaheuristics solving the JSSP. First, we apply a combination of 'attribute-oriented induction' and 'association rule mining' techniques to extract the rules behind the optimal or near-optimal schedules of JSSP. Then, a novel method called 'Assignment Procedure' is proposed to heuristically solve the JSSP using the extracted rules. The proposed method is able to generate numerous schedules for a given JSSP instance, and consequently, the generated solutions can be considered as the initial population for population-based solution methods. To evaluate the effectiveness of the proposed method, the two well-known metaheuristics are considered, i.e., genetic algorithm (GA) and particle swarm optimization (PSO). Extensive experiments were carried out on 35 benchmark problem instances, and Friedman test with post hoc Nemenyi test was performed to statistically analyze the results. The results revealed that using the initial populations generated by the proposed approach is very promising compared to the randomly generated population for both GA and PSO. Moreover, the experiments verify the significant amount of FEs that can be saved using the proposed approach and the superiority of the proposed method in comparison with the method of Koonce and Tsai (Comput Ind Eng 38:361–374, 2000) and four well-known dispatching rules in the literature. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of fault diagnosis performed on a mechanical system, based on acquired vibrations from bearings. In this aim, an optimization algorithm resulted from the alliance between a time–frequency–scale signal processing method (the matching pursuit) and an evolutionary computing technique (mainly, a genetic algorithm) is introduced. The matching pursuit method itself leads to a NP-hard procedure, but, with the help of a metaheuristic, the procedure becomes computationally efficient. A generalization of Baker's procedure implementing the stochastic universal sampling mechanism, as well as a new concept, namely the Boltzmann annealing selection, is introduced, in order to design the genetic algorithm appropriately. This latter not only plays an important role in convergence speed, but also constitutes the basis of a (self) adaptive mechanism aiming to keep in balance the exploration and exploitation features. Based on the optimal solution found through the genetic matching pursuit procedure, the bearings fault diagnosis can successfully be performed, even in case of multiple defects and without prior training of some defect classification model. [ABSTRACT FROM AUTHOR]

Most of the meta-heuristic algorithms are based on the natural processes. They were inspired by physical, biological, social, chemical, social-biological, biological-geography, music, and hybrid processes. In this paper, artificial atom algorithm which was inspired by one of natural processes was applied to traveling salesman problem. The obtained results have shown that for small-scale TSP, artificial atom algorithm is closer to optimum than the other compared heuristic algorithms such as tabu search, genetic algorithm, particle swarm optimization, ant colony optimization, and their different combinations. [ABSTRACT FROM AUTHOR]

Behavioral teaching procedures can be used to promote the individualized learning of reading skills for children, and computational processes can assist instructors in the generation of a set of tasks. However, the automatic generation of these tasks can be unfeasible due to the high-order search space for the possible combinations of tasks; this complexity increases when considering the possible constraints as well as adapting the tasks to the individual characteristics of each student. This paper presents a new method to automatically generate teaching matching-to-sample tasks, adapting the difficulty by using bio-inspired optimization metaheuristics. Genetic algorithms, ant colony optimization, and integer and categorical particle swarm optimization were evaluated to determine their stability and capacity to generate adapted tasks. A comparison of the results between the algorithms showed a better rate of convergence for the genetic algorithms, which were able to generate tasks at an adapted level of difficulty to students. These tasks were applied to a group of students at a Brazilian public school in the early stages of a literacy course indicating satisfactory effects in the individual learning process. [ABSTRACT FROM AUTHOR]

In this paper, a recently proposed metaheuristic optimization technique called hybrid flower pollination algorithm (HFPA) is applied to design wideband infinite impulse response digital differentiators (DDs) and digital integrators (DIs). In recent years, benchmark nature-inspired optimization algorithms such as particle swarm optimization (PSO), simulated annealing, and genetic algorithm have been employed for the design of wideband DDs and DIs. However, individually, these algorithms show major drawbacks such as premature convergence, thus leading to a sub-optimal solution. HFPA, however, is a hybrid approach which combines the efficient exploitation and exploration capabilities of two different metaheuristics, namely PSO and flower pollination algorithm (FPA), respectively. The HFPA-based designs have been compared with real-coded genetic algorithm, PSO, differential evolution, success-history-based adaptive differential evolution with linear population size reduction (L-SHADE), self-adaptive differential evolution (jDE), and FPA-based designs with respect to the solution quality, robustness, convergence, and optimization time. Simulation results demonstrate that among all the algorithms, the HFPA-based designs consistently achieve superior performances in the least number of function evaluations. Exhaustive experimentations are conducted to determine the best values of the control parameters of HFPA for the optimal design of DDs and DIs. The proposed designs also outperform the recently reported designs based on non-optimal, classical, and nature-inspired optimization approaches in terms of magnitude response. The lower orders of the proposed designs render them suitable for real-time applications. [ABSTRACT FROM AUTHOR]

In this paper, a connected multidimensional maximum bisection problem is considered. This problem is a generalization of a standard NP-hard maximum bisection problem, where each graph edge has a vector of weights and induced subgraphs must be connected. We propose two metaheuristic approaches, a genetic algorithm (GA) and an electromagnetism-like metaheuristic (EM). The GA uses modified integer encoding of individuals, which enhances the search process and enables usage of standard genetic operators. The EM, besides standard attraction-repulsion mechanism, is extended with a scaling procedure, which additionally moves EM points closer to local optima. A specially constructed penalty function, used for both approaches, is performed as a practical technique for temporarily including infeasible solutions into the search process. Both GA and EM use the same local search procedure based on 1-swap improvements. Computational results were obtained on instances from literature with up to 500 vertices and 60,000 edges. EM reaches all known optimal solutions on small-size instances, while GA reaches all known optimal solutions except for one case. Both proposed methods give results on medium-size and large-scale instances, which are out of reach for exact methods. [ABSTRACT FROM AUTHOR]

The solutions to many optimization paradigms arising from different application domains can be modeled as a tree graph, in such a way that nodes represent the variables to be optimized and edges evince topological relationships between such variables. In these problems the goal is to infer an optimal tree graph interconnecting all nodes under a measure of topological fitness, for which a wide portfolio of exact and approximative solvers have hitherto been reported in the related literature. In this context a research line of interest in the last few years has been focused on the derivation of solution encoding strategies suited to deal with the topological constraints imposed by tree graph configurations, particularly when the encoded solution undergoes typical operators from Evolutionary Computation. Almost all contributions within this research area focus on the use of standard crossover and mutation operators from Genetic Algorithms onto the graph topology beneath encoded individuals. However, the pace at which new evolutionary operators have emerged from the community has grown much sharply during the last decade. This manuscript elaborates on the topological heritability of the so-called Dandelion tree encoding approach under non-conventional operators. This experimental application-agnostic-based study gravitates on the topological transmission of Dandelion-encoded solutions under a certain class of multi-parent crossover operators that lie at the core of the family of $$(\mu +1)$$ evolution strategies and in particular, the so-called Harmony Search algorithm. Metrics to define topological heritability and respect will be defined and evaluated over a number of convergence scenarios for the population of the algorithm, from which insightful conclusions will be drawn in terms of the preserved structural properties of the newly produced solutions with respect to the initial Dandelion-encoded population. [ABSTRACT FROM AUTHOR]

We study a capacitated multi-facility location-allocation problem in which the customers have stochastic demands based on Bernoulli distribution function. We consider the capacitated sub-sources of facilities to satisfy demands of customers. In the discrete stochastic problem, the goal is to find optimal locations of facilities among candidate locations and optimal allocations of existing customers to operating facilities so that the total sum of fixed costs of operating facilities, allocation cost of the customers, expected values of servicing and outsourcing costs is minimized. The model is formulated as a mixed-integer nonlinear programming problem. Since finding an optimal solution may require an excessive amount of time depending on nonlinear constraints, we transform the nonlinear constraints of the problem to linear ones to arrive at a simple formulation of the model. Numerical results show that the LINGO 9.0 software is capable of solving small size problems. For medium and large-size problems, we propose two meta-heuristic algorithms, namely a genetic algorithm and a discrete version of colonial competitive algorithm. Computational results show that the proposed algorithms efficiently obtain effective solutions. [ABSTRACT FROM AUTHOR]

In this paper, for the first time, multi-modal genetic algorithms (MMGAs) are proposed to optimize the resource constrained multi-project scheduling problem (RCMPSP). In problems where the landscape has both multiple local and global optima, such as the RCMPSP, a MMGAs approach can provide managers with an advantage in decision-making because they can choose between alternative solutions equally good. Alternative optima are achieved because the diversification techniques of MMGAs introduce diversity in population, decreasing the possibility of the optimization process getting caught in a unique local or global optimum. To compare the performance of a MMGAs approach with other alternative approaches, commonly accepted by researchers to solve the RCMPSP such as classical genetic algorithms and dispatching heuristics based on priority rules, we analyse two time-based objective functions (makespan and average percent delay) and three coding systems [random keys (RK), activity list (AL), and a new proposal called priority rule (PR)]. We have found that MMGAs significantly improve the efficacy (the algorithm's capability to find the best optimum) and the multi-solution-based efficacy (the algorithm's capability to find multiple optima) of the other two approaches. For makespan the PR is the best code in terms of the efficacy and multi-solution-based efficacy, and the RK is the best code for the average percent delay. [ABSTRACT FROM AUTHOR]

Nowadays, wireless sensor networks (WSNs) are considered in many fields of application. In this paper, we study how to efficiently deploy relay nodes into previously established static WSNs, with the purpose of optimising two relevant factors for the industry: average energy consumption of the sensors and average sensitivity area provided by the network. This is the so-called relay node placement problem, which is a known NP-hard optimisation problem in the literature. With the purpose of tackling this multiobjective (MO) optimisation problem, we consider two different approaches of the trajectory algorithm MO-VNS, assuming a wide range of stop conditions. Two additional standard genetic algorithms are included in this study, NSGA-II and SPEA2, which belong to evolutionary algorithms. The aim is to analyse the behaviour of MO-VNS compared to traditional methodologies. To this end, the four metaheuristics are applied to solve a freely available data set. The results obtained are analysed following a widely accepted statistical methodology and considering three MO quality metrics: hypervolume, set coverage, and attainment surface. After studying the results, we conclude that MO-VNS provides better performance than the standard algorithms NSGA-II and SPEA2. Moreover, we verify that the addition of relay nodes is a good way to optimise traditional WSNs. [ABSTRACT FROM AUTHOR]

Clustering is a popular data analysis technique, which is applied for partitioning of datasets. The aim of clustering is to arrange the data items into clusters based on the values of their attributes. Magnetic charge system search (MCSS) algorithm is a new meta-heuristic optimization algorithm inspired by the electromagnetic theory. It has been proved better than other meta-heuristics. This paper presents a new hybrid meta-heuristic algorithm by combining both MCSS and particle swarm optimization (PSO) algorithms, which is called MCSS-PSO, for partitional clustering problem. Moreover, a neighborhood search strategy is also incorporated in this algorithm to generate more promising solutions. The performance of the proposed MCSS-PSO algorithm is tested on several benchmark datasets and its performance is compared with already existing clustering algorithms such as K-means, PSO, genetic algorithm, ant colony optimization, charge system search, chaotic charge system search algorithm, and some PSO variants. From the experimental results, it can be seen that performance of the proposed algorithm is better than the other algorithms being compared and it can be effectively used for partitional clustering problem. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new parallel optimization algorithm that combines ideas from the fields of metaheuristics and Systolic Computing. The algorithm, called Systolic Genetic Search (SGS), is designed to explicitly exploit the high degree of parallelism available in modern Graphics Processing Unit (GPU) architectures. In SGS, solutions circulate synchronously through a grid of processing cells, which apply adapted evolutionary operators on their inputs to compute their outputs that are then ejected from the cells and continue moving through the grid. Four different variants of SGS are experimentally studied for solving two classical benchmarking problems and a real-world application. An extensive experimental analysis, which considered several instances for each problem, shows that three of the SGS variants designed are highly effective since they can obtain the optimal solution in almost every execution for the instances and problems studied, as well as they outperform a Random Search (sanity check) and two Genetic Algorithms. The parallel implementation on GPU of the proposed algorithm has achieved a high performance obtaining runtime reductions from the sequential implementation that, depending on the instance considered, can arrive to around a hundred times, and have also exhibited a good scalability behavior when solving highly dimensional problem instances. [ABSTRACT FROM AUTHOR]

A cellular genetic algorithm (cGA) is a powerful metaheuristic that has been successfully used since its creation to solve optimization problems. Over the past few years, interest in hybrid metaheuristics has also grown considerably. Research into cross fertilization between algorithms has provided extremely efficient search techniques in the past. In this paper we present a new way of hybridizing a metaheuristic through active components of other metaheuristics. We also introduce a novel methodology for identifying what an active component is. The active components detected are later inserted in a host metaheuristic so as to enhance its performance with regards to efficiency and accuracy (computational symbiosis). In the approach presented here we enhance a cGA, the host metaheuristic, with identified active components of other metaheuristics. After using this computational symbiosis, we analyze the performance of the new resulting algorithms by evaluating them on a set of different well-known discrete problems. The results obtained are objectively satisfactory in efficacy and efficiency. [ABSTRACT FROM AUTHOR]

This study focuses on the reactive scheduling in manufacturing systems. In practical situations, the predetermined schedule often changes under the influence of the unpredictable or variable environment in the factory. The reactive scheduling (RS) is a methodology to modify the predetermined schedule to cope with variable situations without suspending the proceeding of the processes on the schedule. Therefore, the RS requires a method to improve the schedule with fast computational time. To obtain an effective method for the RS, this paper proposes a novel RS method using local clustering organization. Also, this paper examines its effectiveness by a numerical experiment. Our results suggest that the proposed method is more effective than the method using genetic algorithm. [ABSTRACT FROM AUTHOR]

The main goal of the research direction is to extract building blocks of knowledge from a problem domain. Once extracted successfully, these building blocks are to be used in learning more complex problems of the domain, in an effort to produce a scalable learning classifier system (LCS). However, whilst current LCS (and other evolutionary computation techniques) discover good rules, they also create sub-optimum rules. Therefore, it is difficult to separate good building blocks of information from others without extensive post-processing. In order to provide richness in the LCS alphabet, code fragments similar to tree expressions in genetic programming are adopted. The accuracy-based XCS concept is used as it aims to produce maximally general and accurate classifiers, albeit the rule base requires condensation (compaction) to remove spurious classifiers. Serendipitously, this work on scalability of LCS produces compact rule sets that can be easily converted to the optimum population. The main contribution of this work is the ability to clearly separate the optimum rules from others without the need for expensive post-processing for the first time in LCS. This paper identifies that consistency of action in rich alphabets guides LCS to optimum rule sets. [ABSTRACT FROM AUTHOR]