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Uncertain multi-modal transport path optimization (UMTPO) is a combined optimization non-deterministic polynomial-time hard problem. Its goal is to determine a path with the lowest total transportation cost and carbon emissions from the starting point to the destination. To effectively address this issue, this article proposes a modified dung beetle optimizer (DBO) to address it. DBO is a swarm-based metaheuristic optimization algorithm that has the features of a fast convergence rate and high solution accuracy. Despite this, the disadvantages of weak global exploration capability and falling easily into local optima exist. In this article, we propose a modified DBO called MSHDBO for function optimization and to solve the UMTPO problem. However, for the vast majority of metaheuristic algorithms, they are designed for continuous problems and cannot directly solve discrete problems. Therefore, this article employs a priority based encoding and decoding method to solve the UMTPO problem. To verify the performance and effectiveness of the MSHDBO algorithm, we compared it with other improved versions of the DBO algorithm used in the literature. We confirmed the excellent performance of MSHDBO using 41 benchmark test functions from the IEEE CEC 2017 test suite and IEEE CEC 2022 test suite. Additionally, we compared the MSHDBO algorithm with 10 other state-of-the-art metaheuristic optimization algorithms through a practical UMTPO problem. The experimental results indicated that the MSHDBO algorithm achieved very good performance when solving the UMTPO problem. [ABSTRACT FROM AUTHOR]

The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a modification of it and based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. In the proposed Covariance Matrix Adaptation Population-Based Incremental Learning (CMA-PBIL) algorithm, the probability distribution of population is described by two parameters: the covariance matrix and the probability vector. The comparison of algorithms was performed in the discrete domain of the solution space, where we used the well-known knapsack problem in a variety of data correlations. The results obtained show that the proposed CMA-PBIL algorithm can perform better than standard PBIL in some cases. Therefore, the proposed algorithm can be a reasonable alternative to the PBIL algorithm in the discrete space domain. [ABSTRACT FROM AUTHOR]

Reliability-based design optimization (RBDO) of a mechanism is normally based on the non-probabilistic model, which is viewed as failure possibility constraints in each optimization loop. It leads to a double-loop nested problem that causes a computationally expensive evaluation. Several methods have been developed to solve the problem, which are expected to increase the realization of optimum results and computational efficiency. The purpose of this paper was to develop a new technique of RBDO that can reduce the complexity of the double-loop nested problem to a single-loop. This involves using a multi-objective evolutionary technique combined with the worst-case scenario and fuzzy sets, known as a multi-objective, reliability-based design optimization (MORBDO). The optimization test problem and a steering linkage design were used to validate the performance of the proposed technique. The proposed technique can reduce the complexity of the design problem, producing results that are more conservative and realizable. [ABSTRACT FROM AUTHOR]

Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market information. In this work, we propose a two-stage stochastic portfolio optimization model with a comprehensive set of real-world trading constraints to address this issue. Our model incorporates the market uncertainty in terms of future asset price scenarios based on asset return distributions stemming from the real market data. Compared with existing models, our model is more reliable since it encompasses real-world trading constraints and it adopts CVaR as the risk measure. Furthermore, our model is more practical because it could help investors to design their future investment strategies based on their future asset price expectations. In order to solve the proposed stochastic model, we develop a hybrid combinatorial approach, which integrates a hybrid algorithm and a linear programming (LP) solver for the problem with a large number of scenarios. The comparison of the computational results obtained with three different metaheuristic algorithms and with our hybrid approach shows the effectiveness of the latter. The superiority of our model is mainly embedded in solution quality. The results demonstrate that our model is capable of solving complex portfolio optimization problems with tremendous scenarios while maintaining high solution quality in a reasonable amount of time and it has outstanding practical investment implications, such as effective portfolio constructions. [ABSTRACT FROM AUTHOR]

Protein structure prediction is the prediction of the 3D structure of a protein based on its amino acid sequence. It is a key component in disciplines such as medicine, biology, and biochemistry. The prediction of the protein secondary structure of Homo sapiens is one of the more important domains. Many methods have been used to feed forward neural networks or SVMs combined with a sliding window. This method's mechanisms are too complex to be able to extract clear and straightforward physical meanings from it. This paper explores population-based incremental learning (PBIL), which is a method that combines the mechanisms of a generational genetic algorithm with simple competitive learning. The result shows that its accuracies are particularly associated with the Homo species. This new perspective reveals a number of different possibilities for the purposes of performance improvements. [ABSTRACT FROM AUTHOR]

This paper aims to study the comparative performance of original multi-objective population-based incremental learning (MPBIL) and three improvements of MPBIL. The first improvement of original MPBIL is an opposite-based concept, whereas the second and third method enhance the performance of MPBIL using the multi and adaptive learning rate, respectively. Four classic multi-objective structural topology optimization problems are used for testing the performance. Furthermore, these topology optimization problems are improved by the method of multiple resolutions of ground elements, which is called a multi-grid approach (MG). Multi-objective design problems with MG design variables are then posed and tackled by the traditional MPBIL and its improved variants. The results show that using MPBIL with opposite-based concept and MG approach can outperform other MPBIL versions. [ABSTRACT FROM AUTHOR]

Owing to large production capacity and high efficiency, multibridge machining systems (MBMSs) have gained increasing attention in industry. Their multiple bridge machines work concurrently in their serially arranged and partially overlapping workspaces. To solve totally the job scheduling and collision resolution problems of MBMS, our prior work proposes a serial-colored traveling salesman problem (S-CTSP)-based method. Each salesman in S-CTSP visits his exclusive cities and some cities shared with his neighbor(s). To endow MBMS with fault-tolerance ability, this paper presents a two-stage method for both static path planning and dynamic collision avoidance of multiple machines. At the first stage, the path planning problem abstracted as an S-CTSP is solved by a population-based incremental learning (PBIL) algorithm. The PBIL introduces a local search operation, two types of possibility vectors, and a selection strategy of exclusive and shared cities. The second stage uses a Petri net supervisor to dynamically avoid any emerging collision when performing the scheduled work. This work also provides a novel priority net structure to prevent mutual waiting of two neighboring machines ready to enter their overlapping workspace. Then, we apply the presented method to a large tribridge waterjet cutting case to show its performance. [ABSTRACT FROM AUTHOR]

Population-based incremental learning (PBIL) has recently received increasing attention due to its effectiveness, easy implementation and robustness. Despite this, recent literature suggests that PBIL may suffer from issues of loss of diversity in the population, resulting in premature convergence. In this paper, three diversity maintaining PBIL methods are proposed to address the issue of loss of diversity in PBIL. The first method uses an adaptive learning rate as opposed to the fixed learning rate that is normally used in the standard PBIL. In this method, the learning rate is adapted according to the degree of evolution of the search space. That is, the learning rate is increased linearly with the number of generation. The second method uses two sub-populations and conduct independent search in parallel with the same initial probability vectors, but with fixed learning rates similar to the one used in the standard PBIL. In the third method, the concept of duality or opposition is combined with parallel PBIL as a means of controlling the diversity in the population. To evaluate the performances of these methods, they are applied to the problem of controller design in power systems to improve the small-signal stability. Simulations results show that the proposed diversity maintaining methods are able to maintain the diversity in the population longer than the standard PBIL. However, PBIL with adaptive learning rate takes a little bit time to converge (more function evaluations) compared to the other methods. [ABSTRACT FROM AUTHOR]

In this paper, multi-objective optimization of a rack-and-pinion steering linkage is proposed. This steering linkage is a common mechanism used in small cars with three advantages as it is simple to construct, economical to manufacture, and compact and easy to operate. In the previous works, many researchers tried to minimize a steering error but minimization of a turning radius is somewhat ignored. As a result, a multi-objective optimization problem is assigned to simultaneously minimize a steering error and a turning radius. The design variables are linkage dimensions. The design problem is solved by the hybrid of multi-objective population-based incremental learning and differential evolution with various constraint handling schemes. The new design strategy leads to effective design of rack-and-pinion steering linkages satisfying both steering error and turning radius criteria. [ABSTRACT FROM AUTHOR]

Genetic algorithm (GAs) have attracted considerable interest for their usefulness in solving complex robot path planning problems. Specifically, researchers have combined conventional GAs with problem-specific operators and initialization techniques to find the shortest paths in a variety of robotic environments. Unfortunately, these approaches have exhibited inherently unstable performance, and they have tended to make other aspects of the problem-solving process (e.g., adjusting parameter sensitivities and creating high-quality initial populations) unmanageable. As an alternative to conventional GAs, we propose a new population-based incremental learning (PBIL) algorithm for robot path planning, a probabilistic model of nodes, and an edge bank for generating promising paths. Experimental results demonstrate the computational superiority of the proposed method over conventional GA approaches. [ABSTRACT FROM AUTHOR]

In this article, real-code population-based incremental learning (RPBIL) is extended for multi-objective optimization. The optimizer search performance is then improved by integrating a mutation operator of evolution strategies and an approximate gradient into its computational procedure. RPBIL and its variants, along with a number of established multi-objective evolutionary algorithms, are then implemented to solve four multi-objective design problems of trusses. The design problems are posted to minimize structural mass and compliance while fulfilling stress constraints. The comparative results based on a hypervolume indicator show that the proposed hybrid RPBIL is the best performer for the large-scale truss design problems. [ABSTRACT FROM PUBLISHER]

Population-based evolutionary computation (EC) is widely used to optimize embedding parameters in intelligent watermarking systems. Candidate solutions generated with these techniques allow finding optimal embedding parameters of all blocks of a cover image. However, using EC techniques for full optimization of a stream of high-resolution grayscale face images is very costly. In this paper, a blockwise multi-resolution clustering (BMRC) framework is proposed to reduce this cost. During training phase, solutions obtained from multi-objective optimization of reference face images are stored in an associative memory. During generalization operations, embedding parameters of an input image are determined by searching for previously stored solutions of similar sub-problems in memory, thereby eliminating the need for full optimization for the whole face image. Solutions for sub-problems correspond to the most common embedding parameters for a cluster of similar blocks in the texture feature space. BMRC identifies candidate block clusters used for embedding watermark bits using the robustness score metric. It measures the texture complexity of image block clusters and can thereby handle watermarks of different lengths. The proposed framework implements a multi-hypothesis approach by storing the optimization solutions according to different clustering resolutions and selecting the optimal resolution at the end of the watermarking process. Experimental results on the PUT face image database show a significant reduction in complexity up to 95.5 % reduction in fitness evaluations compared with reference methods for a stream of 198 face images. [ABSTRACT FROM AUTHOR]

This article deals with the comparative performance of some established multi-objective evolutionary algorithms (MOEAs) for structural topology optimization. Four multi-objective problems, having design objectives like structural compliance, natural frequency and mass, and subjected to constraints on stress, etc., are posed for performance testing. The MOEAs include Pareto archive evolution strategy (PAES), population-based incremental learning (PBIL), non-dominated sorting genetic algorithm (NSGA), strength Pareto evolutionary algorithm (SPEA), and multi-objective particle swarm optimization (MPSO). The various MOEAs are implemented to solve the problems. The ground element filtering (GEF) technique is used to suppress checkerboard patterns on topologies. The results obtained from the various optimizers are illustrated and compared. It is shown that PBIL is far superior to the others. The optimal topologies from using PBIL can be compared with those obtained by employing the classical gradient-based approach. It can be considered as a powerful tool for structural topological design. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems. [ABSTRACT FROM AUTHOR]

We propose a method for concept-based medical image retrieval that is a superset of existing semantic-based image retrieval methods. We conceive of a concept as an incremental and interactive formalization of the user's conception of an object in an image. The premise is that such a concept is closely related to a user's specific preferences and subjectivity and, thus, allows to deal with the complexity and content-dependency of medical image content. We describe an object in terms of multiple continuous boundary features and represent an object concept by the stochastic characteristics of an object population. A population-based incrementally learning technique, in combination with relevance feedback, is then used for concept customization. The user determines the speed and direction of concept customization using a single parameter that defines the degree of exploration and exploitation of the search space. Images are retrieved from a database in a limited number of steps based upon the customized concept. To demonstrate our method we have performed concept-based image retrieval on a database of 292 digitized X-ray images of cervical vertebrae with a variety of abnormalities. The results show that our method produces precise and accurate results when doing a direct search. In an open-ended search our method efficiently and effectively explores the search space. [ABSTRACT FROM AUTHOR]

Additive clustering was originally developed within cognitive psychology to enable the development of featural models of human mental representation. The representational flexibility of additive clustering, however, suggests its more general application to modeling complicated relationships between objects in non-psychological domains of interest. This paper describes, demonstrates, and evaluates a simple method for learning additive clustering models, based on the combinatorial optimization approach known as Population-Based Incremental Learning. The performance of this new method is shown to be comparable with previously developed methods over a set of ‘benchmark’ data sets. In addition, the method developed here has the potential, by using a Bayesian analysis of model complexity that relies on an estimate of data precision, to determine the appropriate number of clusters to include in a model. [ABSTRACT FROM AUTHOR]

Object localization has applications in many areas of engineering and science. The goal is to spatially locate an arbitrarily shaped object. In many applications, it is desirable to minimize the number of measurements collected while ensuring sufficient localization accuracy. In surgery, for example, collecting a large number of localization measurements may either extend the time required to perform a surgical procedure or increase the radiation dosage to which a patient is exposed. Localization accuracy is a function of the spatial distribution of discrete measurements over an object when measurement noise is present. In previous work (J. of Image Guided Surgery, Simon et al., 1995), metrics were presented to evaluate the information available from a set of discrete object measurements. In this study, new approaches to the discrete point data selection problem are described. These include hillclimbing, genetic algorithms (GAs), and Population-Based Incremental Learning (PBIL). Extensions of the standard GA and PBIL methods that employ multiple parallel populations are explored. The results of extensive empirical testing are provided. The results suggest that a combination of PBIL and hillclimbing result in the best overall performance. A computer-assisted surgical system that incorporates some of the methods presented in this paper is currently being evaluated in cadaver trials. [ABSTRACT FROM AUTHOR]

Fault diagnostics aims to locate the origin of an abnormity if it presents and therefore maximize the system performance during its full life-cycle. Many studies have been devoted to the feature extraction and isolation mechanisms of various faults. However, limited efforts have been spent on the optimization of sensor location in a complex engineering system, which is expected to be a critical step for the successful application of fault diagnostics. In this paper, a novel sensor location approach is proposed for the purpose of fault isolation using population-based incremental learning (PBIL). A directed graph is used to model the fault propagation of a complex engineering system. The multidimensional causal relationships of faults and symptoms were obtained via traversing the directed path in the directed graph. To locate the minimal quantity of sensors for desired fault isolatability, the problem of sensor location was firstly formulated as an optimization problem and then handled using PBIL. Two classical cases, including a diesel engine and a fluid catalytic cracking unit (FCCU), were taken as examples to demonstrate the effectiveness of the proposed approach. Results show that the proposed method can minimize the quantity of sensors while keeping the capacity of fault isolation unchanged. [ABSTRACT FROM AUTHOR]