Enhance Algorithm Performance Research Articles

A novel multi-state reinforcement learning-based multi-objective evolutionary algorithm

Multi-objective evolutionary algorithms (MOEAs) are widely employed to tackle multi-objective optimization problems (MOPs). However, the choice of different crossover operators significantly impacts the algorithm's ability to balance population diversity and convergence effectively. To enhance algorithm performance, this paper introduces a novel multi-state reinforcement learning-based multi-objective evolutionary algorithm, MRL-MOEA, which utilizes reinforcement learning (RL) to select crossover operators. In MRL-MOEA, a state model is established according to the distribution of individuals in the objective space, and different crossover operators are designed for the transition between different states. Additionally, in the process of evolution, the population still exhibits inadequate convergence in certain regions, leading to sparse areas within the regular Pareto Front (PF). To address this issue, a strategy for adjusting weight vectors has been devised to achieve uniform distribution of the PF. The experimental results of MRL-MOEA on several benchmark suites with a varying number of objectives ranging from 3 to 10, including WFG and DTLZ, demonstrate MRL-MOEA's competitiveness compared to other algorithms.

Trie-PMS8: A trie-tree based robust solution for planted motif search problem

Finding patterns in biological sequences is a crucial and intriguing task. This paper explores the (Ɩ, d) motif search problem, also known as Planted Motif Search (PMS), and discusses its challenging nature as an NP-hard problem. PMS and (Ɩ, d) motif search algorithms are believed to represent the next generation of tools for motif discovery. In this context, PMS deals with n biological sequences and two parameters, Ɩ and d, to identify sequences of Ɩ length that occur in all input strings with, at most, d mismatches. Many existing exact PMS algorithms exhibit exponential time complexity in worst-case scenarios. This paper introduces an innovative algorithm that focuses on improving the efficiency of the sample-driven portion of the process. Specifically, dynamic programming techniques are employed to avoid redundant calculations in frequently used subtrees. Furthermore, this paper presents novel approaches to enhance algorithm performance, such as utilizing a trie tree that significantly reduces the time for the “sort rows by size” step. It has also reduced the spaces that take linked lists on LL-PMS8 (Hasan et al., Jun., 2022) or reduced the number of l-mers. Using trie tree as the main way to speed things up gives a much better result than older versions of PMS methods like LL-PMS8 (Hasan et al., Jun., 2022). Overall time complexity reduced than the previous method is 26.17 % and 16.48 % for real-world and generated datasets (Hasan et al., 2020).

Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy

The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection strategy. We divided the population into elite and normal subpopulations based on fitness values. Information exchange between the two subpopulations was facilitated through a hierarchical mutation strategy, promoting a balanced exploration–exploitation trade-off in the algorithm. Additionally, this paper presents a new hierarchical selection strategy aimed at improving the population’s capacity to avoid local optima. It achieves this by accepting discarded trial vectors differently compared to previous methods. We expect that the newly introduced hierarchical selection and mutation strategies will work in synergy, effectively harnessing their potential to enhance the algorithm’s performance. Extensive experiments were conducted on the CEC 2017 and CEC 2011 test sets. The results showed that the DPDE algorithm offers competitive performance, comparable to six state-of-the-art differential evolution algorithms.

A GIS-based maritime supply chain network design of distant-water fisheries

In the pursuit of efficient and cost-effective supply chain management within the marine capture fishery sector, this study introduces a novel approach that integrates the supply chain network for distant-water fisheries. This approach not only involves an integrated maritime location inventory routing problem (MLIRP) but also considers the identification of offshore fishing grounds based on geographic information system (GIS) techniques. The proposed model centers on critical decision variables, including selection of fishery production areas, strategic placement and sizing of perishable goods depots, and allocation of refrigerated vessels. Meanwhile, the model proposes two trade-off objective functions: one seeks to maximize the weighted coverage rate of fishery resource within selected production areas, while the other aims to minimize the total cost of the built network. To address the complex model, a method based on the k-means clustering and the multi-objective plant growth simulation algorithm (MO-PGSA) is adopted. Key elements, such as growth operators, dominant strength, crowding distance, and partially ordered sets, are involved to enhance algorithm performance. Finally, the effectiveness of the proposed model and method are discussed through numerical experiments based on the real-life case in the Spratly Islands.

A Path-Planning Method Based on Improved Soft Actor-Critic Algorithm for Mobile Robots.

The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots' interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method.

Feasibility of Using Zero-Shot Learning in Transformer-Based Natural Language Processing Algorithm for Key Information Extraction from Head and Neck Tumor Board Notes

Natural language processing (NLP) technology has the potential to automate information aggregation and summarization in oncology. One example is the automation of patient registry creation. In this work, we aim to show (1) the feasibility of using modern NLP algorithms to extract key information from tumor board notes, and (2) the impact of prompt engineering on the quality of the results. In this IRB-approved study, we obtained the texts of head and neck tumor board notes for 306 unique patients. Five key pieces of information used to create a patient registry were predefined: age, gender, tumor histology, tumor stage, and primary location. The NLP algorithm used was a modified Text-To-Text Transfer Transformer (T5) model that was initially trained on the Colossal Clean Crawled Corpus (C4) dataset and subsequently fine-tuned on the Stanford Question Answering Dataset (SQuAD) to perform the downstream task of extractive question answering. The NLP model and trained weights were obtained from the Hugging Face platform. During inference, the entire body of the tumor board note and a related question were fed as inputs, and the model predicted a sequence of texts in response to the question. Two sets of questions of similar semantic meanings were used. Questions in prompt set #1 included "What is the gender?", "What is the age?", "What is the type of carcinoma in pathological diagnosis?", "What is the stage?", and "Where is the carcinoma located at?". Questions in prompt set #2 include "Is the patient male or female?", "How old is the patient?", "What kind of cancer?", "What is the cancer stage?", and "What is the tumor location?". Each model-predicted response was compared to the ground truth extracted from the tumor board notes. A response was classified as true if it is consistent with the ground truth, otherwise, it was deemed false. The response accuracy for each question was subsequently calculated. The median number of words in each tumor board note was 448 (range, 219 - 1505). The accuracy of the NLP algorithm for each question from either set is reported in Table 1. Algorithm performance is higher for extracting objective information such as age, gender, and histology. In addition, it was found that questions of similar semantic meanings but with different wording can lead to significantly different results. We demonstrated that a transformer-based extractive question-answering NLP algorithm can be successfully used for extracting information from head and neck tumor board notes with zero-shot learning. Furthermore, our results highlight the significance of prompt engineering for applying NLP for this task. Future work on finetuning these algorithms to oncology-specific texts can potentially enhance algorithm performance for more difficult tasks.

A genetic‐algorithm‐based synthesis of microwave integrated distributed amplifiers

AbstractThis article presents a genetic algorithm‐based technique for microwave integrated distributed amplifier synthesis. To speed‐up synthesis, only linear characteristics are used in the goal function. The technique uses MMIC foundry process design kit models providing manufacturing‐ready schematics to be found. The models from the process design kit are presented as S‐parameters again to enhance algorithm performance. The technique allows searching among most of the existing schematics of the microwave distributed amplifier but is not limited to typical topologies. The section number is theoretically unlimited and makes it possible to find principally new circuit topology. The synthesis shortens and simplifies the distributed amplifier design time. Experimental validation of the technique is carried out by synthesizing the broadband driver stage for the 20–30 GHz buffer amplifier. The technique produces several amplifier topologies meeting the requirements. The most appropriate circuit was manufactured using a commercial 0.25 μm GaAs pHEMT process. The manufactured buffer amplifier demonstrates a gain of 10 dB and 20 dBm output power at a 1 dB compression point.

Vectorization Programming Based on HR DSP Using SIMD

Single instruction multiple data (SIMD) vector extension has become an essential feature of high-performance processors. Architectures such as x86, ARM, MIPS, and PowerPC have specific vector extension instruction sets and SIMD micro-architectures. Using SIMD vectorization programming can significantly improve the performance of application algorithms while keeping the hardware overhead low. In addition, other methods can enhance algorithm performance, such as selecting the best SIMD vectorization model for algorithms, ensuring sufficient instruction streams, implementing reasonable and effective cache data prefetching, and aligning data access and storage addresses according to instruction characteristics. The goal of this paper is three-fold. First, we introduce the basic structural characteristics of a general RISC processor, Hua Rui (HR) DSP, with a custom vector instruction set based on compatibility with an MIPS64 fixed-point and floating-point instruction set, as well as a Fei Teng (FT) processor compatible with an ARMv8 instruction set. Second, we summarize the fundamental principles of SIMD vectorization programming design for the HR DSP, which provides ideas for other scholars or engineering and technical personnel to study the algorithm performance using SIMD vectorization optimization. Third, we implement representative typical algorithms based on the HR and FT platforms and obtain experimental results that show improvement in algorithm SIMD vectorization optimization according to the vector programming design principles summarized in this article can improve the single-core performance of scalar implementation without vectorization, instruction streams, and cache data prefetching by 4–22 times for mean filter, accumulation, and matrix–matrix multiplication, which is significantly better than the performance improvement of 3–13 times for the FT platform. Moreover, the performance of matrix–matrix multiplication using the best vectorization model on the HR platform is about 84% higher than that of the common SIMD vectorization model.

Securing 6G-enabled IoT/IoV networks by machine learning and data fusion

The rapid growth of Internet of Things (IoT) and Internet of Vehicles (IoV) are rapidly moving to the 6G networks, which leads to dramatically raised security issues. Using machine learning, including deep learning, to find out malicious network traffic is one of practical ways. Though much work has been done in this direction, we found little investigating the effect of using fused network conversation datasets to train and test models. Thus, this work proposes to check conversation dataset characteristics and find suitable ones to fuse into one dataset in order to improve the capability of malicious traffic and malware detection performance. The experiments using real data show that conditioned combination of datasets can be used to enhance algorithm performance and improve detection results. For this reason, it is recommended to profile datasets and conduct conditional fusion of network conversation datasets before using machine learning or deep learning. As the characterization is done using general statistical calculation, it is promising to be used for other domains too.

Multi-objective evolutionary algorithms with heuristic decoding for hybrid flow shop scheduling problem with worker constraint

The classical hybrid flow shop scheduling problem (HFSSP) considers the operation and machine constraints but not the worker constraint. Acknowledging the influence and potential of human factors as a key element in improving production efficiency in a real hybrid flow shop, we consider a new realistic HFSSP with worker constraint (HFSSPW) and construct its mixed integer linear programming model. Seven multi-objective evolutionary algorithms with heuristic decoding (HD) (MOEAHs) are proposed to solve the HFSSPW. According to list scheduling, we first present four HD methods for four MOEAHs, and these methods incorporate four priority rules of machine and worker assignments. The earliest due date (EDD) rule is further introduced into the HD methods for the other three MOEAHs. The developed model is solved using CPLEX based on 20 loose instances under a time limit, and the four proposed MOEAHs are evaluated by comparing them with the results from CPLEX and two best-performing algorithms in the literature. The computational results reveal that the proposed MOEAHs perform excellently in terms of the makespan objective. Additionally, comprehensive experiments, including 150 tight instances, are conducted. In terms of solution quality and efficiency, the computational results show that the proposed MOEAHs demonstrate highly effective performance, and integrating EDD into the HD can substantially enhance algorithm performance. Finally, a real-life problem of the foundry plant is solved by MOEAHs and the scheduling solutions totally meet the delivery requirement.

An Optimal Solution for Smooth and Non-Smooth Cost Functions-Based Economic Dispatch Problem

A modified particle swarm optimization and incorporated chaotic search to solve economic dispatch problems for smooth and non-smooth cost functions, considering prohibited operating zones and valve-point effects is proposed in this paper. An inertia weight modification of particle swarm optimization is introduced to enhance algorithm performance and generate optimal solutions with stable solution accuracy and offers faster convergence characteristic. Moreover, an incorporation of chaotic search, called logistic map, is used to increase the global searching capability. To demonstrate the effectiveness and feasibility of the proposed algorithm compared to the several existing methods in the literature, five systems with different criteria are verified. The results show the excellent performance of the proposed method to solve economic dispatch problems.

How do users interact with algorithm recommender systems? The interaction of users, algorithms, and performance

Although algorithms have been widely used to deliver useful applications and services, it is unclear how users actually experience and interact with algorithm-driven services. This ambiguity is even more troubling in news recommendation algorithms, where thorny issues are complicated. This study investigates the user experience and usability of algorithms by focusing on users' cognitive process to understand how qualities/features are received and transformed into experiences and interaction. This work examines how users perceive and feel about issues in news recommendations and how they interact and engage with algorithm-recommended news. It proposes an algorithm experience model of news recommendation integrating the heuristic process of cognitive, affective, and behavioral factors. The underlying algorithm can affect in different ways the user's perception and trust of the system. The heuristic affect occurs when users' subjective feelings about transparency and accuracy act as a mental shortcut: users considered transparent and accurate systems convenient and useful. The mediating role of trust suggests that establishing algorithmic trust between users and NRS could enhance algorithm performance. The model illustrates the users' cognitive processes of perceptual judgment as well as the motivation behind user behaviors. The results highlight a link between news recommendation systems and user interaction, providing a clearer conceptualization of user-centered development and the evaluation of algorithm-based services.

Continuous monitoring and detection of post-traumatic stress disorder (PTSD) triggers among veterans: A supervised machine learning approach

Post-traumatic stress disorder (PTSD) is a prevalent mental health condition among United States combat veterans, associated with high incidence of suicide and substance abuse. While PTSD treatments exist, such methods are limited to in-person therapy sessions and medications. Tools and technologies to monitor patients continuously, especially between sessions, are largely absent. This article documents efforts to develop predictive algorithms that utilize real-time heart rate data, collected using commercial off-the-shelf wearable sensors, to detect the onset of PTSD triggers. The heart rate data, pre-processed with a Kalman filter imputation approach to resolve missing data, were used to train five algorithms: decision tree, support vector machine, random forest, neural network, and convolutional neural network. Prediction performance was assessed with the Area Under the receiver operating characteristic Curve (AUC). The convolutional neural network, support vector machine, and random forests had the highest AUC and significantly outperformed a random classifier. Further analysis of the heart rate data and predictions suggest that the algorithms associate an increase in heart rate with PTSD trigger onset. While work is needed to enhance algorithm performance and robustness, these results suggest that wearable monitoring technology for PTSD symptom mitigation is an achievable goal in the near future.

Criteria to evaluate the fidelity of image enhancement by MSRCR

Image fidelity refers to the ability of a process to render an image accurately. As image enhancement algorithms have been developed in recent years, how to assess the performances of different image enhancement algorithms has become an important question. Some objective image quality assessment (IQA) methods have been proposed, but there is little research on the image fidelity evaluation when comparing the performances of enhancement algorithms. Therefore, the authors proposed a new image fidelity assessment framework consisting of three components: the information entropy fidelity, constituent fidelity and colour fidelity. To verify the rationality of the fidelity criteria, they used the popular IQA database (LIVE), and the results indicated that the method matched better with the subjective assessment. Then, they verified the effectiveness of their method with the famous technique of image enhancement: multi-scale Retinex with colour restoration (MSRCR). The experimental results demonstrate MSRCR can improve the image quality, but it gives rise to obvious distortions. It is necessary to keep a moderate balance between image fidelity and image quality when they assess the enhanced images. Their results showed that the proposed objective fidelity index could provide an additional objective basis for the quality evaluation of image enhancement algorithms.

On the use of two reference points in decomposition based multiobjective evolutionary algorithms

Decomposition based multiobjective evolutionary algorithms approximate the Pareto front of a multiobjective optimization problem by optimizing a set of subproblems in a collaborative manner. Often, each subproblem is associated with a direction vector and a reference point. The settings of these parameters have a very critical impact on convergence and diversity of the algorithm. Some work has been done to study how to set and adjust direction vectors to enhance algorithm performance for particular problems. In contrast, little effort has been made to study how to use reference points for controlling diversity in decomposition based algorithms. In this paper, we first study the impact of the reference point setting on selection in decomposition based algorithms. To balance the diversity and convergence, a new variant of the multiobjective evolutionary algorithm based on decomposition with both the ideal point and the nadir point is then proposed. This new variant also employs an improved global replacement strategy for performance enhancement. Comparison of our proposed algorithm with some other state-of-the-art algorithms is conducted on a set of multiobjective test problems. Experimental results show that our proposed algorithm is promising.

Noise estimation for speech enhancement algorithms with post-smoothness processor incorporating global posterior SNR

Performances of speech enhancement algorithms depend greatly on the accuracy of the estimated noise. In this paper, we explain in details the relationship between noise estimation and denoised speech quality. We particularly show the importance of noise smoothing over frames on denoising quality. This study leads to the development of a new technique to smooth the estimated noise power spectrum over frequency bins of the same frame. Compared to inter-frame smoothing, experimental results show that the proposed intra-frame smoothing has a good impact on the denoised speech. Quality is evaluated over three dimensions: speech distortion, residual background noise and overall quality.

Improvements in the Ant Colony Optimization Algorithm for Endmember Extraction From Hyperspectral Images

Endmember extraction is a vital step in spectral unmixing of hyperspectral images. The Ant Colony Optimization (ACO) algorithm has been recently developed for endmember extraction from hyperspectral data. However, this algorithm may result in a local optimal solution for some hyperspectral images without prescient information, and also has limitation in computational performance. Therefore, in this paper, we proposed several new methods to improve the ACO algorithm for endmember extraction (ACOEE). Firstly, the heuristic information was optimized to improve the algorithm accuracy. In the improved ACOEE, only the pheromones were adopted as the heuristic information when there was no prescient information about hyperspectral data. Then, to enhance algorithm performance, an elitist strategy was proposed to lessen the iteration numbers without reducing the accuracy, and the parallel implementation of ACOEE on graphics processing units (GPUs) also was utilized to shorten the computational time per iteration. The experiment for real hyperspectral data demonstrated that both the endmember extraction accuracy and the computational performance of ACOEE benefited from these methods.

Fully linear reconstruction method for fluorescence yield and lifetime through inverse complex-source formulation: simulation studies.

In fluorescence imaging, both fluorescence yield and lifetime are of great importance. Traditionally, with the frequency-domain data, two parameters can be directly recovered through a nonlinear formulation. However, the reconstruction accuracy highly depends on initial guesses. To overcome this hurdle, we propose the linear scheme via an inverse complex-source formulation. Using the real and imaginary parts of the frequency-domain data, the proposed method is fully linear; it is not sensitive to initial guesses and is stable with high-level noise. Meanwhile, the algorithm is efficient, and the reconstruction takes one or a few iterations. In addition, the colocalization constraint due to the unique feature of fluorescence imaging is imposed to enhance algorithm performance. The algorithms are tested with simulated data.

Sparse Matrix Block-Cyclic Realignment on Distributed Memory Machines

Modifying the data distribution over the course of a program to adapt to variations in the data access patterns may leads to significant computational benefits in many scientific applications. Therefore, dynamic realignment of data is used to enhance algorithm performance in parallel programs on distributed memory machines. This paper presents a new method aims to the efficiency of block-cyclic data realignment of sparse matrix. The main idea of the proposed technique is first todevelop closed forms for generating the Vector Index Set (VIS) of each source/destination processor. Based on the vector index set and the nonzero structure of sparse matrix, two efficient algorithms,vector2message (v2m) and message2vector (m2v) can be derived. The proposed technique uses v2m to extract nonzero elements from source compressed structures and packs them into messages in the source stage; and uses m2v to unpack each received messages and construct the destination matrix in the destination stage. A significant improvement of this approach is that a processor does not need to determine the complicated sending or receiving data sets for dynamic data redistribution. The indexing cost is reduced obviously. The second advantage of the present techniques is the achievement of optimal packing/unpacking stages consequent upon the informative VIS tables. Another contribution of our methods is the ability to handle sparse matrix redistribution under two disjoint processor grids in the source and destination phases. A theoretical model to analyze the performance of the proposed technique is also presented in this work. To evaluate the performance of our methods, we have implemented the present algorithms on an IBM SP2 parallel machine along with the Histogram method and a dense redistribution strategy. The experimental results show that our technique provides significant improvement for runtime data redistribution of sparse matrices in most test samples.

A Compressed Diagonals Remapping Technique for Dynamic Data Redistribution on Banded Sparse Matrix

In many scientific applications, dynamic data redistribution is used to enhance algorithm performance and achieve data locality in parallel programs on distributed memory multi-computers. In this paper, we present a new method, Compressed Diagonals Remapping (CDR) technique aims to the efficiency of runtime data redistribution on banded sparse matrices. The main idea of the proposed technique is first to compress the source matrix into a Compressed Diagonal Matrix (CDM) form. Based on the compressed diagonal matrix, a one-dimensional local and global index transformation method can be applied to carry out data redistribution on the compressed diagonal matrix. This process is identical to redistribute data in the two-dimensional banded sparse matrix. The CDR technique uses an efficient one-dimensional indexing scheme to perform data redistribution on banded sparse matrix. A significant improvement of this approach is that a processor does not need to determine the complicated sending or receiving data sets for dynamic data redistribution. The indexing cost is reduced significantly. The second advantage of the present techniques is the achievement of optimal packing/unpacking stages consequent upon the consecutive attribute of column elements in a compressed diagonal matrix. Another contribution of our methods is the ability to handle sparse matrix redistribution under two disjoint processor grids in the source and destination phases. A theoretical model to analyze the performance of the proposed technique is also presented in this paper. To evaluate the performance of our methods, we have implemented the present techniques on an IBM SP2 parallel machine along with the v2m algorithm and a dense redistribution strategy. The experimental results show that our technique provides significant improvement for runtime data redistribution of banded sparse matrices in most test samples.