Population-based Algorithm Research Articles

An adaptive biogeography-based optimization with cumulative covariance matrix for rule-based network intrusion detection

Biogeography-based optimization (BBO) is a well-known population-based metaheuristic optimization algorithm. The historical population distribution information is so underutilized that the global optimal value may not be searched efficiently in existing variants of BBO. To address the limitation, we propose in this paper a BBO framework based on cumulative covariance matrix (CCM) and dub it as CCM-BBO. The CCM operator is used in CCM-BBO to establish Eigen coordinate system and enables the migration operator independent on fixed coordinate system. By embedding CCM operator into BBO, the migration operator is implemented in the Eigen coordinate system to enhance the rotation invariance of BBO. To verify the performance of CCM operator, CCM-BBO has been applied to three state-of-the-art variants of BBO: SBBO, DE/BBO and EMBBO. The experimental results on 25 benchmark functions of CEC2005, 10 benchmark functions of CEC2020, and a real-world intrusion detection optimization problem have demonstrated that CCM-BBO is an effective framework to enhance the performance of BBO.

An evolutionary correlation-aware feature selection method for classification problems

As global search techniques, population-based optimization algorithms have provided promising results in feature selection (FS) problems. However, their major challenge is high time complexity associated with the exploration of a large search space and consequently a large number of fitness function evaluations. Moreover, the interaction between features is another key issue in FS problems, directly affecting the classification performance through selecting correlated features. In this paper, an estimation of distribution algorithm (EDA)-based method is proposed with three important contributions. Firstly, as an extension of EDA, the proposed method in each iteration generates only two individuals competing based on a fitness function, evolving during the algorithm using our proposed update procedure. Secondly, we provide a guiding technique to determine the number of features to be selected for individuals in each iteration. As a result, the number of selected features in the final solution would be optimized during the evolution process. These two would lead to increasing the convergence speed of the algorithm. Thirdly, as the main contribution of the paper, in addition to considering the importance of each feature alone, the proposed method can consider the interaction between features, being able to deal with complementary features and consequently increase classification performance. To do this, we provide a conditional probability scheme that considers the joint probability distribution of selecting two features. The introduced probabilities successfully detect correlated features. Experimental results on a synthetic dataset with correlated features proved the performance of our proposed approach facing these types of features. Furthermore, the results on 13 real-world datasets obtained from the UCI repository showed the superiority of the proposed method in comparison with some state-of-the-art approaches. To evaluate the effectiveness of each feature subset, support vector machines are used as classifier. The efficiency analysis of the experimental results using two non-parametric statistical tests proved that the proposed method had significant advantages in comparison to other approaches.

Comparison of algorithms for multi-objective optimization of radio technical device characteristics

Objectives. The selection of a method for solving multi-objective optimization problems has many practical applications in diverse fields. The present work compares the results of applying different methods to the selected classes of problems by solution quality, time consumption, and various other criteria.Methods. Five problems related to the multi-objective optimization of analog and digital filters, as well as multistep impedance-matching microwave transformers, are considered. One of the compared algorithms comprises the Third Evolution Step of Generalized Differential Evolution (GDE3) population-based algorithm for searching the full approximation of the Pareto set simultaneously, while the other three algorithms minimize the scalar objective function to find only one element of the Pareto set in a single search cycle: these comprise Multistart Pattern Search (MSPS), Multistart Sequential Quadratic Programming (MSSQP) method and Particle Swarm Optimization (PSO) algorithms.Results. The computer experiments demonstrated the capability of GDE3 to solve all considered problems. MSPS and PSO showed significantly inferior results than to GDE3 for two problems. In one problem, MSSQP could not be used to reach acceptable decisions. In the other problems, MSPS, MSSQP, and PSO reached decisions comparable with GDE3. The time consumption of the MSPS and PSO algorithms was much greater than that of GDE3 and MSSQP.Conclusions. The GDE3 algorithm may be recommended as a basic method for solving the considered problems. Algorithms minimizing scalar objective function may be used to obtain several elements of the Pareto set. It is necessary to investigate the impact of landscape features of individual quality indices and scalar objective functions on the extreme search process.

A Novel Self-Adaptive Cooperative Coevolution Algorithm for Solving Continuous Large-Scale Global Optimization Problems

Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process (“on fly”) from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC’2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals.

Intermodal transportation hub location optimization with governments subsidies under the Belt and Road Initiative

Driven by globalization, the COVID-19 outbreak has severely impacted global transport and logistics systems. To better cope with this globalization crisis, the Belt and Road Initiative (BRI)—based on the concept of cooperation—is more important than ever in the post-pandemic era. Taking the BRI as the background, we design an intermodal hub-and-spoke network to provide reference for governments along BRI routes to improve their cross-border transportation system and promote economic recovery. In the context of the BRI, local governments at different nodes have incentives to subsidize hub construction and/or rail transportation to boost economic development. We consider co-opetition behavior among different levels of government caused by subsidies in this intermodal hub location problem, which we call the intermodal hub location problem based on government subsidies. We establish a two-stage mixed-integer programming model. In the first stage, local governments provide subsidies, then the central government decides the number and location of hubs. In the second stage, freight carriers choose the optimal route to transport the goods. To solve the model, we design an optimization method combining a population-based algorithm using contest theory. The results show that rail subsidies are positively correlated with construction subsidies but are not necessarily related to the choice of hubs. Compared with monomodal transportation, intermodal transportation can reduce costs more effectively when there are not too many hubs and the cost of different modes of transportation varies greatly. The influences of local government competition and hub construction investment on network design and government subsidies are further examined.

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.

Employing Quantum Fruit Fly Optimization Algorithm for Solving Three-Dimensional Chaotic Equations

In a chaotic system, deterministic, nonlinear, irregular, and initial-condition-sensitive features are desired. Due to its chaotic nature, it is difficult to quantify a chaotic system’s parameters. Parameter estimation is a major issue because it depends on the stability analysis of a chaotic system, and communication systems that are based on chaos make it difficult to give accurate estimates or a fast rate of convergence. Several nature-inspired metaheuristic algorithms have been used to estimate chaotic system parameters; however, many are unable to balance exploration and exploitation. The fruit fly optimization algorithm (FOA) is not only efficient in solving difficult optimization problems, but also simpler and easier to construct than other currently available population-based algorithms. In this study, the quantum fruit fly optimization algorithm (QFOA) was suggested to find the optimum values for chaotic parameters that would help algorithms converge faster and avoid the local optimum. The recommended technique used quantum theory probability and uncertainty to overcome the classic FA’s premature convergence and local optimum trapping. QFOA modifies the basic Newtonian-based search technique of FA by including a quantum behavior-based searching mechanism used to pinpoint the position of the fruit fly swarm. The suggested model has been assessed using a well-known Lorenz system with a specified set of parameter values and benchmarked signals. The results showed a considerable improvement in the accuracy of parameter estimates and better estimation power than state-of-the art parameter estimation approaches.

Differential Evolution and Its Applications in Image Processing Problems: A Comprehensive Review.

Differential evolution (DE) is one of the highly acknowledged population-based optimization algorithms due to its simplicity, user-friendliness, resilience, and capacity to solve problems. DE has grown steadily since its beginnings due to its ability to solve various issues in academics and industry. Different mutation techniques and parameter choices influence DE's exploration and exploitation capabilities, motivating academics to continue working on DE. This survey aims to depict DE's recent developments concerning parameter adaptations, parameter settings and mutation strategies, hybridizations, and multi-objective variants in the last twelve years. It also summarizes the problems solved in image processing by DE and its variants.

Population-Based Meta-Heuristic Algorithms for Integrated Batch Manufacturing and Delivery Scheduling Problem

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time.

An intelligent edge-enabled distributed multi-task learning architecture for large-scale IoT-based cyber–physical systems

Internet-of-Things (IoT)-based cyber–physical systems are increasingly being adopted because of the recent technological advancements in sensor technology, edge computing, machine learning, and big data. Integrating machine learning into designing IoT-based cyber–physical systems is essential. However, it is considered a challenging problem. This stems from the fact that IoT devices generate extensive data that requires extensive processing to achieve adequate learning. Relying on local learning by each IoT device is not feasible in most cases due to its limited resources. On the contrary, relying on all cloud-based learning requires transmitting a large amount of data to the cloud to perform the learning process, which is inefficient in large-scale IoT deployments. Therefore, this paper proposes a novel edge-computing architecture that employs the concept of distributed multi-task learning over EC networks in large-scale IoT-based cyber–physical systems. The architecture develops multiple distributed learning algorithms, a data placement architecture, task allocation algorithms, and a network protocol. In addition, it considers the problem of learning model parameters from IoT data distributed over different edge nodes in a large geographical area without sending raw data to the cloud. The architecture supports several distributed machine models that are trained using a combination of machine learning algorithms and population-based search algorithms to optimize the learning process. Population-based search algorithms allow for maintaining a set of candidate solutions, with each solution corresponding to a unique point in the search space for an optimal solution. Having the dataset distributed over several edge nodes, with each node having its own unique set of candidate solutions, increases the chance of finding a solution that generalizes well for the overall dataset combined. Simulation experiments with real IoT datasets are conducted to evaluate the accuracy of the proposed learning models. Results show the ability to achieve high-accuracy results that are close to single-machine models but with significantly efficient edge computing resource utilization.

Application of Fine-Tuned Krill Herd Algorithm in Design of Water Distribution Networks

Stochastic population-based metaheuristic algorithms have been widely used to design water distribution networks (WDNs). The present study introduces one of its kind, the krill herd algorithm (KHA), a novel swarm intelligence-based metaheuristic. Demonstrating KHA application to WDN design, the study proposes its enhanced version to improve its convergence precision. Consequently, combining the fine-tuned mechanism of KHA with the EPANET 2.2 simulation software, the fine-tuned KHA (FIT-KHA) model is formulated and checked for its efficiency in handling the WDN design problem. Because three benchmark problems (new and rehabilitation WDNs) of different dimensions are considered, the significance of the algorithm control parameters concerning the network size is studied. From sensitivity analysis, the time step factor, number of krill, and maximum iteration size are found to influence the algorithm performance. Their optimal values followed an increasing trend with the network size. The computational results reinforce a superior search exploitation ability of the FIT-KHA. Importantly, the results exhibit a better computational efficiency of KHA over most of the reported metaheuristic algorithms. Then, the performance state of optimally designed WDN under hydraulic (demand, roughness coefficient variations) and mechanical (single- and two-pipe failure) uncertain scenarios is studied. Besides reliability and surrogate measures, multiaspect metrics based on adequacy and equity are evaluated to assess the network’s performance. The WDNs are observed to be more susceptible to demand uncertainties over roughness coefficient variation. At higher states of mechanical (pipe) failure scenarios, especially, the equity of the WDN is disturbed compared to the adequacy, except for rehabilitated WDN. Certainly, the performance study manifests that the reliability measure that truly presents the WDNs performance is the function of network type. While proposing KHA as an effective alternative optimization tool, the study suggests a prior performance study to choose an appropriate metric to formulate a reliability-based multiobjective framework for the robust design of WDNs.

A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms

A Hierarchical Method of Parameter Setting for Population-Based Metaheuristic Optimization Algorithms

Optimized Network Reconfiguration with Integrated Generation Using Tangent Golden Flower Algorithm

The importance of integrating distributed generation (DG) units into the distribution network (DN) recently developed. To decrease power losses (PL), this article presents a meta-heuristic population-based tangent golden flower pollination algorithm (TGFPA) as an optimization technique for selecting the ideal site for DG. Furthermore, the proposed algorithm also finds the optimal routing configuration for power flow. TGFPA requires very few tuning parameters and is comprised of a golden section and a tangent flight algorithm (TFA). Hence, it is easy to update these parameters to obtain the best values, which provide highly reliable results compared to other existing techniques. In different case studies, the TGFPA’s performance was assessed on four test bus systems: IEEE 33-bus, IEEE 69-bus, IEEE 119-bus, and Indian-52 bus. According to simulation results, TGFPA computes the optimal reconfigured DN embedded along with DG, achieving the goal of minimal power loss.

Compatible Swarm Intelligence Model and Construction Method to Satisfy Bridge Design and Load Rating Requirements and Considering Geotechnical Variability

This paper details the novel combination of a population-based swarm intelligence algorithm, structure mechanics, geotechnical variability, and a specific construction staging methodology, to propose an autonomous construction system which can erect a bridge over arbitrary crossing geometry while satisfying project constraints and conventional design code requirements (the Proposal). The fields of metaheuristic applications in structures (MAS), unmanned aerial systems (UAS), and additive manufacturing are all independent innovations in the transportation industry. The Proposal brings these fields together, with a vision that independent drone robots with very basic programming could construct a bridge with no user interruption and without performing a conventional structural design. A simulation as proof-of-concept is compared with a modern bridge design; the result is a modest increase in materials with an anticipated dramatic saving in labor. The paper also studies the effects of geotechnical variability for insight into preparation for possible future construction methodologies and algorithms. The Proposal has promise where socio-economic issues may be a factor, such as construction of infrastructure in remote locations or developing nations, and emergency repair or replacement of bridges. With recent U.S. policy supporting a permanent lunar presence and expanding Moon to Mars exploration through the Artemis program, the Proposal may also assist infrastructure development in advance of human missions.

Stochastic process and tutorial of the African buffalo optimization

This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. Organizing their large herds that could be over a thousand buffalos using just two principal sounds, the /maaa/ and the /waaa/ calls present a good foundation for the development of an optimization algorithm. Since elaborate descriptions of the manual workings of optimization algorithms are rare in literature, this paper aims at solving this problem, hence it is our main contribution. It is our belief that elaborate manual description of the workings of optimization algorithms make it user-friendly and encourage reproducibility of the experimental procedures performed using this algorithm. Again, our ability to describe the algorithm’s basic flow, stochastic and data generation processes in a language so simple that any non-expert can appreciate and use as well as the practical implementation of the popular benchmark Rosenbrock and Shekel Foxhole functions with the novel algorithm will assist the research community in benefiting maximally from the contributions of this novel algorithm. Finally, benchmarking the good experimental output of the ABO with those of the popular, highly effective and efficient Cuckoo Search and Flower Pollination Algorithm underscores the ABO as a worthy contribution to the existing body of population-based optimization algorithms

Infrared Image Enhancement Based on Optimally Weighted Multi-Scale Laplacian of Gaussian and Local Statistics Using Particle Swarm Optimization

Infrared imagery is extensively used in defense, remote sensing and medical applications. While the infrared images have many advantages over RGB images, the details in these images are usually blurred which in turn leads to some difficulties for human operators. In this paper, a new method based on Laplacian of Gaussian scale-space and local variance is presented to improve the visual quality of the infrared images. At the first step, the Gaussian scale-space is constructed by convolving the original image with different Gaussian kernels. Then, the two-dimensional Laplacian kernels are convolved with the Gaussian scale-space to achieve details with both positive as well as negative contrasts. The weighted details are added to the original image to deblur the dim areas. At the final step, to increase the dynamic range of the image and have better visual quality, the local variance of the image is also added to the output of the previous step. Since finding optimum weighting coefficients is a difficult task empirically, here, we use a population-based meta-heuristic optimization algorithm called particle swarm optimization (PSO) to find the optimum values for weighting coefficient values. Beside qualitative comparison, Structural Similarity (SSIM) and second-derivative-like measure of enhancement (SDME) are used to quantitatively investigate the images quality. The proposed method outperforms the baseline algorithms in both qualitative and quantitative perspectives.

Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization

The transmission of high-speed data over communication channels is the function of digital communication systems. Due to linear and nonlinear distortions, data transmitted through this process is distorted. In a communication system, the channel is the medium through which signals are transmitted. The useful signal received at the receiver becomes corrupted because it is associated with noise, ISI, CCI, etc. The equalizers function at the front end of the receiver to eliminate these factors, and they are designed to make them work efficiently with proper network topology and parameters. In the case of highly dispersive and nonlinear channels, it is well known that neural network-based equalizers are more effective than linear equalizers, which use finite impulse response filters. An alternative approach to training neural network-based equalizers is to use metaheuristic algorithms. Here, in this work, to develop the symmetry-based efficient channel equalization in wireless communication, this paper proposes a modified form of bat algorithm trained with ANN for channel equalization. It adopts a population-based and local search algorithm to exploit the advantages of bats’ echolocation. The foremost initiative is to boost the flexibility of both the variants of the proposed algorithm and the utilization of proper weight, topology, and the transfer function of ANN in channel equalization. To evaluate the equalizer’s performance, MSE and BER can be calculated by considering popular nonlinear channels and adding nonlinearities. Experimental and statistical analyses show that, in comparison with the bat as well as variants of the bat and state-of-the-art algorithms, the proposed algorithm substantially outperforms them significantly, based on MSE and BER.

A Novel Ensemble Machine Learning and an Evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this article, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic’s behavior. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multiobjective optimization algorithm is developed, which uses the model to search through the policy space and finds a set of policies that minimize the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modeling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this article to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a metamodel algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The metamodel is a set of support vector machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimization process, which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimization algorithms are performed.

Integration of group method of data handling (GMDH) algorithm and population-based metaheuristic algorithms for spatial prediction of potential groundwater

Integration of group method of data handling (GMDH) algorithm and population-based metaheuristic algorithms for spatial prediction of potential groundwater

Hierarchical Approaches to Solve Optimization Problems

Optimization is the operation of finding the most appropriate solution for a particular problem or set of problems. In the literature, there are many population-based optimization algorithms for solving optimization problems. Each of these algorithms has different characteristics. Although optimization algorithms give optimum results on some problems, they become insufficient to give optimum results as the problem gets harder and more complex. Many studies have been carried out to improve optimization algorithms to overcome these difficulties in recent years. In this study, six well-known population-based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA, and particle swarm optimization - PSO) were used. Each of these algorithms has its own advantages and disadvantages. These population-based six algorithms were tested on CEC’17 test functions and their performances were examined and so the characteristics of the algorithms were determined. Based on these results, hierarchical approaches have been proposed in order to combine the advantages of algorithms and achieve better results. The hierarchical approach refers to the successful operation of algorithms. In this study, eight approaches were proposed, and performance evaluations of these structures were made on CEC’17 test functions. When the experimental results are examined, it is concluded that some hierarchical approaches can be applied, and some hierarchical approaches surpass the base states of the algorithms.