Search

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 treatment of left turns at signalized intersections drives the development of signal phasing and timing plans and also plays an important role in overall traffic network operations. Accommodating left turns allows for the most direct routeing but reduces intersection capacity, whereas restricting left turns improves capacity but requires some vehicles to travel longer distances. This paper proposes a population-based incremental learning (PBIL) algorithm to determine at which intersections left-turn restrictions should be enacted to maximize a network's operational performance. The performance of each configuration is tested in a micro-simulation environment on both perfect and imperfect square grid networks. Comparison with a partial enumeration of feasible options reveals that the PBIL algorithm is effective at identifying a near-optimal configuration of left-turn restrictions. The resulting configurations suggest that left turns should be generally restricted at intersections that carry the most flow. These intersections typically occur in the central portion of the network when demands are relatively uniform. Doing so helps to provide additional intersection capacity at the locations where it is most necessary, while minimizing the additional travel distance that is incurred due to detours caused by the left-turn restrictions. These provide insight as to how urban traffic networks might be managed to improve network efficiency by only enacting left-turn restrictions at a subset of locations. [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]

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.

Network-On-Chip (NoC) platforms were proposed to increase system performance in current and future generations of Multi-Processor System-on-Chip ranging from a few cores to hundreds. For such platforms, efficient mechanisms to perform the mapping of executable tasks are required in order to improve metrics such as execution time, latency, energy, and others. The Population-Based Incremental Learning (PBIL) algorithm has been used as an optimization technique for the mapping of tasks onto the cores of NoC platforms. However, it does not scale well in terms of latency and other relevant metrics when the size of the platform and the number of tasks are increased. In this work, we propose a new approach, that relies on the PBIL algorithm, for the mapping of tasks called Virtual Regions PBIL (VRPBIL-NoC). This strategy consists of dividing the platform into virtual regions in order to improve the search of quality solutions. We evaluate the performance of our technique by comparing it against a set of heuristic techniques available in the literature, using an extended version of a well-known state-of-the-art simulator called Noxim. The results demonstrated that our approach can deliver better solutions compared to those provided by the other techniques in NoCs for varied configurations. [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]

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.

The reloading of a nuclear power plant is a procedure that becomes necessary whenever the energy produced by the fuel assemblies’ burnup is not sufficient to keep the plant working at its full power, which means an interruption on the production of energy. The longer is the time spent on performing the reloading, the higher is the loss of money. Due to the great complexity involved in this process, it is not feasible to solve it by classical methods such as hill climbing or gradient ones. For this reason, the optimization methods known as evolutionary algorithms such as the genetic algorithms have been used as an alternative to the traditional practices. Genetic algorithms use the biological metaphor of the survival of the fittest proposed by Darwin to motivate its mechanism of individuals composing a population. Based on these concepts, Baluja has proposed the Population-Based Incremental Learning (PBIL), a stochastic optimization technique. This paper presents a review study related to the evolution of PBIL when used to optimize the nuclear reload process of the 7th operation cycle of the Brazilian PWR Angra 1. Since 1999, several researchers have been using the most varied optimization techniques which analyze this particular case study. Therefore, this survey aims to describe in which ways the different strategies adopted by some of PBIL's variations has contributed to obtain better results along the years on solving this particular problem, besides making a comparison of the results obtained by each of them. [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]

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.

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]

The focus of this research is to develop a classifier using an artificial immune system (AIS) combined with population-based incremental learning (PBIL) and collaborative filtering (CF) for network intrusion detection. AIS is a powerful tool in terms of extirpating antigens inspired by the principles and processes of the natural immune system. PBIL uses past experiences to evolve into new species through learning and adopting the idea of CF for classification. The novelty of this research is in its combining of the three above mentioned approaches to develop a new classifier which can be applied to detect network intrusion, with incremental learning capability, by adapting the weight of key features. In addition, four mechanisms: creating a new antibody using PBIL, dynamic adjustment of feature weighting using clonal expansion, antibody hierarchy adjustment using mean affinity, as well as usage rates, are proposed to intensify AIS performance. As shown by the comparison carried out with other artificial intelligence and evolutionary computation approaches in network anomaly detection problems, our PBIL-AIS CF classifier can achieve high accuracy for the benchmark problem. [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]

Abstract: This work proposes the use of a new optimization method based on a parameters self-learning mechanism in association with the basic structure of Quantum PBIL (QPBIL) named Self-adaptive Quantum PBIL (SQPBIL). It has been tested to solve an issue of great interest in nuclear engineering commonly known as the Nuclear Reload Optimization Problem (NROP) which consists on optimizing the reactor's core fuel reloading. The NROP is defined as a search for an optimum configuration of shuffling among the fresh fuel assemblies and the remaining ones from previous reloading processes. According to the particular case analyzed in this study, the aim is to make the time between reloads as greater as possible, having in mind safety and geometric restrictions. SQPBIL was applied to the particular case of the Pressurized Water Reactor (PWR) NROP to maximize the 7th operation's cycle of Angra 1. The results have confirmed the efficiency of the method in comparison to other optimization techniques based on artificial intelligence currently available. [Copyright &y& Elsevier]

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]