Cardiovascular disease (CVD) is the leading cause of death worldwide. A Machine Learning (ML) system can predict CVD in the early stages to mitigate mortality rates based on clinical data. Recently, many research works utilized different machine learning approaches to detect CVD or identify the patient’s severity level. Although these works obtained promising results, none focused on employing optimization methods to improve the ML model performance for CVD detection and severity-level classification. This study provides an effective method based on the Synthetic Minority Oversampling Technique (SMOTE) to handle imbalance distribution issue, six different ML classifiers to detect the patient status, and Hyperparameter Optimization (HPO) to find the best hyperparameter for ML classifier together with SMOTE. Two public datasets were used to build and test the model using all features. The results show that SMOTE and Extra Trees (ET) optimized using hyperband achieved higher results than other models and outperformed the state-of-the-art works by achieving 99.2% and 98.52% in CVD detection, respectively. Also, the developed model converged to 95.73% severity classification using the Cleveland dataset. The proposed model can help doctors determine a patient’s current heart disease status. As a result, it is possible to prevent heart disease-related mortality by implementing early therapy.

An Effective Heart Disease Detection and Severity Level Classification Model Using Machine Learning and Hyperparameter Optimization Methods

Heterogeneous cognitive learning particle swarm optimization for large-scale optimization problems

Multimodal optimization aiming at locating multiple global optima at a time has received extensive attention from researchers since it can afford multiple choices for decision-makers. To effectively locate as many optima of multimodal optimization problems (MMOPs) as possible, this paper proposes a novel differential evolution framework, named proximity ranking-based multimodal differential evolution (PRMDE). Firstly, a proximity ranking-based individual selection method is proposed to randomly select parent individuals involved in the mutation operation. Specifically, instead of the classical uniform selection, this paper devises a non-linear weight function to calculate the selection probabilities of individuals according to their proximity rankings and then randomly selects parent individuals based on the roulette wheel selection strategy. In this way, each individual is likely mutated by its Euclidian neighbors. Secondly, an adaptive parameter adjustment strategy is further devised for the selection probability calculation, so that the selection probabilities of closer individuals to each target individual gradually increase as the evolution continues. Thirdly, an adaptive local search strategy is designed to carry out the Gaussian distribution based local search adaptively around individuals. In this way, better individuals have higher chances to conduct local search to subtly improve their quality. By means of the cohesive cooperation among the three main mechanisms, PRMDE is expectedly capable of simultaneously locating multiple optima of MMOPs. Theoretically, any mutation strategy can be embedded into PRMDE to deal with MMOPs. Four classical mutation strategies are adopted in this paper to instantiate PRMDE. Experiments carried out on the publicly acknowledged CEC2013 benchmark MMOP set demonstrate that PRMDE is effective to solve MMOPs and attains considerably competitive or even far better optimization performance than several representative and state-of-the-art multimodal optimization methods. 

Proximity ranking-based multimodal differential evolution

In high dimensional environment, the interaction among particles significantly affects their movements in searching the vast solution space and thus plays a vital role in assisting particle swarm optimization (PSO) to attain good performance. To this end, this paper designs a random contrastive interaction (RCI) strategy for PSO, resulting in RCI-PSO, to tackle large-scale optimization problems (LSOPs) effectively and efficiently. Unlike existing interaction mechanisms for low-dimensional problems, RCI randomly chooses several different peers from the current swarm to construct a random interaction topology for each particle. Then, it lets the particle interact with the selected peers based on their current evolutionary information instead of their historical evolutionary information. Within the topology, RCI only propagates the evolutionary information of two contrastive dominators with the largest difference in fitness to direct the evolution of the particle. Therefore, particles with no more than two dominators in their topologies are not updated. Furthermore, a dynamic topology size adjustment scheme is devised to gradually enlarge the interaction topology. In this way, the swarm gradually switches from exploring the immense search space dispersedly to exploiting the found optimal regions intensively as the evolution continues. With these two strategies, RCI-PSO expectedly compromises search diversity and search convergence well at the swarm level and the particle level. At last, extensive experiments executed on two public LSOP suites verify that RCI-PSO performs competitively with or even much better than totally 40 state-of-theart large-scale approaches and preserves a good capability and scalability in tackling complex LSOPs. 

Random Contrastive Interaction for Particle Swarm Optimization in High-Dimensional Environment

Differential evolution (DE) has been experimentally demonstrated to be effective in solving optimization problems. However, the effectiveness of DE encounters rapid deterioration in the face of complicated optimization problems with many grotesque local basins, which are emerging increasingly frequently nowadays. To alleviate this predicament, this paper devises a simple yet effective mutation scheme named “DE/current-to-rwrand/1” to further promote the optimization ability of DE in solving complicated optimization problems. Specifically, this strategy assigns a non-linear selection probability for each individual, which is computed based on its function value ranking. As a result, all individuals can be potentially selected to direct the mutation of the population, but better individuals preserve exponentially larger selection probabilities. In this way, the resultant DE, which is named function value ranking aware differential evolution (FVRADE), is expectedly capable of balancing high diversity and fast convergence of the population well to find satisfactory solutions to optimization problems. In particular, two classical and popular mutation strategies, namely “DE/current-to-rand/1” and “DE/current-to-best/1”, are two special cases of the proposed mutation strategy. Abundant experiments have been extensively executed on the well-known CEC’2017 and the latest CEC’2021 benchmark problem sets. Experimental results have verified that FVRADE performs highly competitively with or even significantly better than several representative and state-of-the-art peer methods. In particular, it is experimentally demonstrated that FVRADE is particularly good at solving complicated optimization problems, and preserves a good scalability to deal with optimization problems. Besides, experiments have also been conducted on the popularly adopted CEC’2011 real-world optimization problem suite and the experimental results have substantiated that FVRADE is very promising to solve real-world optimization problems. 

Function value ranking aware differential evolution for global numerical optimization

Random neighbor elite guided differential evolution for global numerical optimization

Particle swarm optimization (PSO) has exhibited well-known feasibility in problem optimization. However, its optimization performance still encounters challenges when confronted with complicated optimization problems with many local areas. In PSO, the interaction among particles and utilization of the communication information play crucial roles in improving the learning effectiveness and learning diversity of particles. To promote the communication effectiveness among particles, this paper proposes a stochastic triad topology to allow each particle to communicate with two random ones in the swarm via their personal best positions. Then, unlike existing studies that employ the personal best positions of the updated particle and the neighboring best position of the topology to direct its update, this paper adopts the best one and the mean position of the three personal best positions in the associated triad topology as the two guiding exemplars to direct the update of each particle. To further promote the interaction diversity among particles, an archive is maintained to store the obsolete personal best positions of particles and is then used to interact with particles in the triad topology. To enhance the chance of escaping from local regions, a random restart strategy is probabilistically triggered to introduce initialized solutions to the archive. To alleviate sensitivity to parameters, dynamic adjustment strategies are designed to dynamically adjust the associated parameter settings during the evolution. Integrating the above mechanism, a stochastic triad topology-based PSO (STTPSO) is developed to effectively search complex solution space. With the above techniques, the learning diversity and learning effectiveness of particles are largely promoted and thus the developed STTPSO is expected to explore and exploit the solution space appropriately to find high-quality solutions. Extensive experiments conducted on the commonly used CEC 2017 benchmark problem set with different dimension sizes substantiate that the proposed STTPSO achieves highly competitive or even much better performance than state-of-the-art and representative PSO variants.

/pdf/stochastic-triad-topology-based-particle-swarm-optimization-250yi0w9.pdf

Stochastic Triad Topology Based Particle Swarm Optimization for Global Numerical Optimization

High-dimensional optimization problems are more and more common in the era of big data and the Internet of things (IoT), which seriously challenge the optimization performance of existing optimizers. To solve these kinds of problems effectively, this paper devises a dimension group-based comprehensive elite learning swarm optimizer (DGCELSO) by integrating valuable evolutionary information in different elite particles in the swarm to guide the updating of inferior ones. Specifically, the swarm is first separated into two exclusive sets, namely the elite set (ES) containing the top best individuals, and the non-elite set (NES), consisting of the remaining individuals. Then, the dimensions of each particle in NES are randomly divided into several groups with equal sizes. Subsequently, each dimension group of each non-elite particle is guided by two different elites randomly selected from ES. In this way, each non-elite particle in NES is comprehensively guided by multiple elite particles in ES. Therefore, not only could high diversity be maintained, but fast convergence is also likely guaranteed. To alleviate the sensitivity of DGCELSO to the associated parameters, we further devise dynamic adjustment strategies to change the parameter settings during the evolution. With the above mechanisms, DGCELSO is expected to explore and exploit the solution space properly to find the optimum solutions for optimization problems. Extensive experiments conducted on two commonly used large-scale benchmark problem sets demonstrate that DGCELSO achieves highly competitive or even much better performance than several state-of-the-art large-scale optimizers.

https://www.mdpi.com/2227-7390/10/7/1072/pdf?version=1648534099

A Dimension Group-Based Comprehensive Elite Learning Swarm Optimizer for Large-Scale Optimization

Although particle swarm optimization (PSO) has been successfully applied to solve optimization problems, its optimization performance still encounters challenges when dealing with complicated optimization problems, especially those with many interacting variables and many wide and flat local basins. To alleviate this issue, this paper proposes a differential elite learning particle swarm optimization (DELPSO) by differentiating the two guiding exemplars as much as possible to direct the update of each particle. Specifically, in this optimizer, particles in the current swarm are divided into two groups, namely the elite group and non-elite group, based on their fitness. Then, particles in the non-elite group are updated by learning from those in the elite group, while particles in the elite group are not updated and directly enter the next generation. To comprise fast convergence and high diversity at the particle level, we let each particle in the non-elite group learn from two differential elites in the elite group. In this way, the learning effectiveness and the learning diversity of particles is expectedly improved to a large extent. To alleviate the sensitivity of the proposed DELPSO to the newly introduced parameters, dynamic adjustment strategies for parameters were further designed. With the above two main components, the proposed DELPSO is expected to compromise the search intensification and diversification well to explore and exploit the solution space properly to obtain promising performance. Extensive experiments conducted on the widely used CEC 2017 benchmark set with three different dimension sizes demonstrated that the proposed DELPSO achieves highly competitive or even much better performance than state-of-the-art PSO variants.

/pdf/differential-elite-learning-particle-swarm-optimization-for-9mce54gc.pdf

Differential Elite Learning Particle Swarm Optimization for Global Numerical Optimization

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