Population-based Metaheuristics Research Articles

Metaheuristic-based energy-aware image compression for mobile app development

AbstractThe widely applied JPEG standard has undergone recent efforts using population-based metaheuristic (PBMH) algorithms to optimise quantisation tables (QTs) for specific images. However, user preferences, like an Android developer’s preference for small-size images, are often overlooked, leading to high-quality images with large file sizes. Another limitation is the lack of comprehensive coverage in current QTs, failing to accommodate all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user’s opinion in the compression process, the file size of the output image can be controlled by a user in advance. To this end, we propose a novel objective function for population-based JPEG image compression. Second, we suggest a novel representation to tackle the lack of comprehensive coverage. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both representation and objective function changes are independent of the search strategies and can be used with any population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.

An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.

In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.

Population based metaheuristics in Spark: Towards a general framework using PSO as a case study

Over the years metaheuristics have been successfully applied to optimization problems in many real-world applications. The increasing complexity and scale of the problems addressed has posed new challenges to researchers in the field. The application of distributed metaheuristics is a common approach to speed up the time to solution or improve its quality by leveraging traditional parallel programming models on platforms like multicore processors or computer clusters. More recently, the emergence of Cloud Computing and new programming models and frameworks for Big Data has facilitated access to an unprecedented amount of computational resources, which led to a growing interest in optimization frameworks that support the development and execution of distributed metaheuristics taking advantage of this potential. In this paper, we present the current status of development of one such framework that aims to provide support for the application of distributed population-based metaheuristics to the global optimization of large-scale problems in Spark. The framework provides a reduced set of abstractions to represent the general structure of population-based metaheuristics as templates and strategies to particularize them into concrete metaheuristics, as well as other nice features like out of the box implementations of the most common distributed models, full configurability through a human-friendly format, and the possibility of rapid prototyping and testing metaheuristics in the Spark shell. To validate the approach, a template for Particle Swarm Optimization (PSO) was implemented as a proof of concept, which includes strategies for instantiating different variants of the algorithm, configurable topologies, and sequential and distributed execution models.

Effective Population-Based Meta-heuristics with NEH and GRASP Heuristics Minimizing Total Weighted flow Time in No-Wait Flow Shop Scheduling Problem Under Sequence-Dependent Setup Time Constraint

Effective Population-Based Meta-heuristics with NEH and GRASP Heuristics Minimizing Total Weighted flow Time in No-Wait Flow Shop Scheduling Problem Under Sequence-Dependent Setup Time Constraint

Time Complexity of Population-Based Metaheuristics

This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamental concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known metaheuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics' time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed.

An improved solution methodology for the urban transit routing problem

An improved solution methodology is proposed in this paper for the urban transit routing problem (UTRP). This methodology includes a procedure for the generation of improved initial solutions as well as improved metaheuristic search approaches, involving the use of hyperheuristics to manage search operators in both trajectory-based and population-based metaheuristics. The UTRP variant considered in this paper is that of deciding upon efficient bus transit routes. The design criteria embedded in our UTRP model are the simultaneous minimisation of the expected average passenger travel time and minimisation of the system operator’s cost (measuring the latter as the sum total of all route lengths in the system). The model takes as input an origin–destination demand matrix for a pre-specified set of bus stops, along with an underlying road network structure, and returns as output a set of bus route trade-off solutions. The decision maker can then select one of these route sets subjectively, based on the desired degree of trade-off between the aforementioned transit system design criteria. This bi-objective minimisation problem is solved approximately in three distinct stages — a solution initialisation stage, an intermediate analysis stage, and an iterative metaheuristic search stage during which high-quality trade-off solutions are sought. A novel procedure is introduced for the solution initialisation stage, aimed at effectively generating high-quality initial feasible solutions. Two metaheuristics are implemented to solve instances of the problem, namely a dominance-based multi-objective simulated annealing algorithm and an improved non-dominated sorting genetic algorithm, each equipped with a hyperheuristic capable of managing the perturbation operators employed. Various novel operators are proposed for these metaheuristics, of which the most noteworthy take into account the demand of passengers.

Deterministic constructive vN-NEH[formula omitted] algorithm to solve permutation flow shop scheduling problem with makespan criterion

Scheduling jobs in a permutation flow shop (PFS) environment is one of the most studied problems in scheduling theory and practice. In this paper, we propose a new efficient algorithm to solve the permutation flow shop scheduling problem (PFSP) with the makespan criterion. The proposed algorithm, referred to as vN-NEH+, extends the Nawaz–Enscore–Ham (NEH) heuristic by employing the variable-length list (vN-list) of candidate jobs. Extensive numerical experiments on the standard set of benchmarks show that the proposed vN-NEH+ algorithm is one of the most efficient NEH-based deterministic constructive algorithms in terms of trade-off between solution quality and computing time. Moreover, in contrary to existing scheduling methods, vN-NEH+ produces in a single run a set of good quality solutions that can serve as an initial population in population-based metaheuristics for solving PFSPs. Another advantage of vN-NEH+ is that it can work in parallel mode, which is difficult or even impossible with other NEH-based heuristics. In addition to vN-NEH+, we propose a new ART.NEH (Average Relative Time over NEH) indicator to assess the running time of PFSP algorithms. The main advantage of ART.NEH is its hardware/software and instance size independence, which allows an objective and reliable comparison of computational effort of PFSP algorithms. In addition, the idea behind ART.NEH can be easily adopted to reliably assess time complexity of algorithms for solving various other computational problems.

Metaheuristics in the Balance: A Survey on Memory-Saving Approaches for Platforms with Seriously Limited Resources

In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have been thoroughly reviewed in several recent survey papers. However, the present survey addresses an important gap in the literature. Here, we reflect on a systematic categorisation of what we call “lightweight” metaheuristics, i.e., optimisation algorithms characterised by purposely limited memory and computational requirements. We focus mainly on two classes of lightweight algorithms: single-solution metaheuristics and “compact” optimisation algorithms. Our analysis is mostly focused on single-objective continuous optimisation. We provide an updated and unified view of the most important achievements in the field of lightweight metaheuristics, background concepts, and most important applications. We then discuss the implications of these algorithms and the main open questions and suggest future research directions.

Differential evolution with alternation between steady monopoly and transient competition of mutation strategies

Real parameter single objective optimization has been studied for decades. In recent, long-term search is emphasized based on the consideration that, in the field, solving difficulty often scales exponentially with the increase of function dimensionality. For long-term search, Differential Evolution (DE) still performs outstanding among types of population-based metaheuristics. In this paper, based on IMODE – a DE algorithm with three mutation strategies, we propose AMCDE – Differential Evolution with Alternation between steady Monopoly and transient Competition of mutation strategies. Our algorithm has two states. In the steady state – monopoly, a selected mutation strategy controls the whole population. Once improvement in fitness becomes difficult, the transient state – competition – arises. In the competition state, similar with IMODE, each of the mutation strategies controls a proportion of positions in the population and competes with the others. For enhancement, we propose that positions controlled by the winner among the three mutation strategies continue to be controlled by the mutation strategy in the next generation. Besides, in the competition state, the original selection strategy of IMODE is revised by us for diversification, while adaptation of the crossover rate is updated. Our experiment is based on three CEC benchmark test suites and the CEC 2011 suite of real world optimization problems. AMCDE is compared with seven peers. Based on experimental results, our algorithm demonstrates superior or at the very least comparable performance for long-term search compared to the peers. Moreover, we do experimental observation on AMCDE.

How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu’s method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds.Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.

An Iterated Population-Based Metaheuristic for Order Acceptance and Scheduling in Unrelated Parallel Machines with Several Practical Constraints

This study considers order acceptance and scheduling problems in unrelated parallel machines with several practical constraints, including order release times, sequence-dependent setup times, machines’ unequal ready times, and preventive maintenance. In a make-to-order production environment, issues with order acceptance and scheduling are mainly caused by the limited production capacity of a factory, which makes it impossible to accept all orders. Consequently, some orders must be rejected in order to maximize profits and the accepted orders must be completed by the due date or no later than the deadline. An iterated population-based metaheuristic is proposed to solve the problems. The algorithm begins with an efficient initial solution generator to generate an initial solution, and then uses the destruction and construction procedure to generate a population with multiple solutions. Then, a solution is selected from the population, and a variable neighborhood descent search algorithm with several new reduced-size neighborhood structures is applied to improve the selected solution. Following the completion of the local search, a method for updating the members of the population was devised to enhance its diversity. Finally, the metaheuristic allows the populations to evolve for several generations until the termination condition is satisfied. To evaluate the performance of the proposed metaheuristic, a heuristic rule and an iterated local search algorithm are examined and compared. The computational experimental results indicate that the presented metaheuristic outperforms the other heuristics.

Intelligence in Finance and Economics for Predicting High-Frequency Data

Forecasting exchange rates is a complex problem that has benefitted from recent advances and research in machine learning. The main goal of this study is to design and implement a method to improve the learning performance of artificial neural networks with large volumes of data using population-based metaheuristics. The micro-genetic training algorithm is thoroughly analyzed using profiling tools to find bottlenecks. We compare the use of a micro-genetic algorithm to predict changes in currency exchange rates on a data set containing more than 500,000 values. To find the best parameters of neural networks, we propose an improved micro-genetic training algorithm by dividing the training data into mini batches. In this case, the improved micro-genetic algorithm proved to be much faster compared to the standard genetic algorithm, while achieving the same prediction accuracy. This allows for the use of this algorithm for just-in-time predictions of high frequency data. Here, neural network models are first created and validated on an existing data set. Then, the new data values can be added to neural network models and retrained in a short time.

Memetic Algorithms for Spatial Partitioning Problems

Spatial optimization problems (SOPs) are characterized by spatial relationships governing the decision variables, objectives, and/or constraint functions. In this article, we focus on a specific type of SOP called spatial partitioning, which is a combinatorial problem due to the presence of discrete spatial units. Exact optimization methods do not scale with the size of the problem, especially within practicable time limits. This motivated us to develop population-based metaheuristics for solving such SOPs. However, the search operators employed by these population-based methods are mostly designed for real-parameter continuous optimization problems. For adapting these methods to SOPs, we apply domain knowledge in designing spatially aware search operators for efficiently searching through the discrete search space while preserving the spatial constraints. To this end, we put forward a simple yet effective algorithm called s warm-based s p atial meme ti c al gorithm (SPATIAL) and test it on the school (re)districting problem. Detailed experimental investigations are performed on real-world datasets to evaluate the performance of SPATIAL. Besides, ablation studies are performed to understand the role of the individual components of SPATIAL. Additionally, we discuss how SPATIAL is helpful in the real-life planning process and its applicability to different scenarios and motivate future research directions.

Trade credit financing for supply chain coordination under financial challenges: a multi-leader–follower game approach

This study is designed to solve supply chain inefficiencies caused by some members' financial problems, such as capital shortages and financing restrictions in a stochastic environment. To this end, we have established a supply chain finance framework by designing two novel coordinating contracts based on trade credit financing for different problem settings. These contracts are modeled in the form of multi-leader Stackelberg games that address horizontal and vertical competition in a supply chain consisting of multiple suppliers and a financially constrained manufacturer. However, previous studies in the trade credit literature have addressed only simple vertical competition, that is, seller-buyer competition. To solve the proposed models, two algorithms were developed by combining population-based metaheuristics, the Nash-domination concept, and the Nikaido-Isoda function. The results demonstrate that the proposed supply chain finance framework can eliminate supply chain inefficiencies and make a large profit for suppliers, as well as the financially constrained manufacturer. Furthermore, the results of the contracts’ analysis showed that if the manufacturer is required to settle its payments to suppliers before the end of the period, the trade credit contract cannot coordinate the supply chain because of a lack of incentive for suppliers. However, if the manufacturer is allowed to extend its payments to the end of the period, the proposed trade credit financing contract can coordinate the supply chain. Finally, the sensitivity analysis results indicate that the worse the financial status of the manufacturer, the more bargaining power suppliers have in determining the contract parameters for more profit.

Portfolio Optimization Problem: A Taxonomic Review of Solution Methodologies

This survey paper provides an overview of current developments for the Portfolio Optimisation Problem (POP) based on articles published from 2018 to 2022. It reviews the latest solution methodologies utilised in addressing POPs in terms of mechanisms and performance. The methodologies are categorised as Metaheuristic, Mathematical Optimisation, Hybrid Approaches, Matheuristic and Machine Learning. The datasets (benchmark, real-world, and hypothetical) utilised in portfolio optimisation research are provided. The state-of-the-art methodologies for benchmark datasets are presented accordingly. Population-based metaheuristics are the most preferred techniques among researchers in addressing the POP. Hybrid approaches is an emerging trend (2018 onwards). The OR-Library is the most widely used benchmark dataset for researchers to compare their methodologies in addressing POP. The research challenges and opportunities are discussed. The summarisation of the published papers in this survey provides an insight to researchers in identifying emerging trends and gaps in this research area.

Metaheuristic–Boltzmannian optimization model: A new methodology for convergence using the Jensen–Shannon metric in continuous optimization problems

We present a new methodology called J–SIGMA to infer state change information using the minimum Jensen–Shannon distance to describe a continuous optimization problem as a Boltzmannian system, as well as its applications to population-based metaheuristics. In general, a Boltzmannian system describes a macrostate from the statistics of the constituent microstate where the minimum energy is the most probable. If we model an optimization problem as a Boltzmann process, the global optimum would be the most probable state. To achieve this, we propose an analytical derivation of the minimum distance to the Boltzmann distribution using the parameters μ and σ 2 to fit this model. As a case study, we implemented the J–SIGMA methodology on three families of population-based metaheuristics: Swarm, Evolutionary, and Estimation of Distribution, and used a set of continuous optimization functions from CEC’17 to evaluate their performance against other metaheuristics of each family. Finally, from a statistical analysis of the convergence performance, the evidence is shown to affirm that the J–SIGMA methodology can significantly improve the convergence performance of the algorithms, regardless of the metaheuristic family to which it belongs.

Visualizing Combinatorial Search Spaces with Low-Dimensional Euclidean Embedding

The low dimensional Euclidean embedding method (LDEE) allows visualizing combinatorial search spaces by mapping to the Euclidean space R k (with k = 2 or 3 in practice). The mapping of a combinatorial search space Ω to R k is obtained by first running the t-SNE (t-distributed stochastic neighbor embedding) algorithm with an appropriate probability distribution used for the space Ω (for example the Mallows distribution for permutation spaces). Subsequently, the vacuum embedding algorithm, proposed in this article, is used to ensure good visual separation of solutions in R k . The LDEE method maps solutions to a regular grid in R k , which can be used for plotting various kinds of information. Apart from solution evaluations or comparisons of multiple objectives, the proposed method can be used for analyzing the behavior of the population in population-based metaheuristics, the working of genetic operators, etc. This newsletter contribution summarizes a recent research article [1].

Generalized Net Model of Coyote Optimization Algorithm

In the presented paper, the functioning of the coyote optimization algorithm (COA) is described using the apparatus of generalized nets (GNs). The COA is a population-based metaheuristic for optimization inspired by the Canis latrans species. Based on a Universal GN-model of population-based metaheuristics, а GN-model of COA is constructed by setting different characteristic functions of the GN-tokens. The presented GN-model successfully describes the considered metaheuristic algorithm, conducting basic steps and performing an optimal search.

Artificial Intelligence-Based Robust Hybrid Algorithm Design and Implementation for Real-Time Detection of Plant Diseases in Agricultural Environments.

The early detection and prevention of plant diseases that are an important cause of famine and food insecurity worldwide are very important for increasing agricultural product productivity. Not only the early detection of the plant disease but also the determination of its type play a critical role in determining the appropriate treatment. The fact that visual inspection, which is frequently used in determining plant disease and types, is tiring and prone to human error, necessitated the development of algorithms that can automatically classify plant disease with high accuracy and low computational cost. In this study, a new hybrid plant leaf disease classification model with high accuracy and low computational complexity, consisting of the wrapper approach, including the flower pollination algorithm (FPA) and support vector machine (SVM), and a convolutional neural network (CNN) classifier, is developed with a wrapper-based feature selection approach using metaheuristic optimization techniques. The features of the image dataset consisting of apple, grape, and tomato plants have been extracted by a two-dimensional discrete wavelet transform (2D-DWT) using wavelet families such as biorthogonal, Coiflets, Daubechies, Fejer-Korovkin, and symlets. Features that keep classifier performance high for each family are selected by the wrapper approach, consisting of the population-based metaheuristics FPA and SVM. The performance of the proposed optimization algorithm is compared with the particle swarm optimization (PSO) algorithm. Afterwards, the classification performance is obtained by using the lowest number of features that can keep the classification performance high for the CNN classifier. The CNN classifier with a single layer of classification without a feature extraction layer is used to minimize the complexity of the model and to deal with the model hyperparameter problem. The obtained model is embedded in the NVIDIA Jetson Nano developer kit on the unmanned aerial vehicle (UAV), and real-time classification tests are performed on apple, grape, and tomato plants. The experimental results obtained show that the proposed model classifies the specified plant leaf diseases in real time with high accuracy. Moreover, it is concluded that the robust hybrid classification model, which is created by selecting the lowest number of features with the optimization algorithm with low computational complexity, can classify plant leaf diseases in real time with precision.

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part I

Scalability of optimization algorithms is a major challenge in coping with the ever-growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly, population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part I of the series covers two major algorithmic approaches to large-scale global optimization: 1) problem decomposition and 2) memetic algorithms. Part II of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally, touches upon the pitfalls and challenges of current research and identifies several potential areas for future research.