Multi-objective particle swarm optimization with random immigrants

Satellite layout optimization design (SLOD) relies on solving a high-dimensional and multimodal optimization problem, in which there exist multiple global optimal solutions. Existing algorithms for SLOD focus on seeking only one approximate global optimum. However, finding multiple solutions simultaneously could provide more design diversity for the designers. To alleviate this problem, multimodal optimization method is studied for SLOD in this paper, and an improved niching-based cross-entropy method (INCE) is proposed. INCE consists of an improved niching strategy, cross-entropy method-based offspring generation and a cross operator. CEC2013 benchmarks and satellite layout optimization design problem are investigated to verify the validity and feasibility of the proposed INCE. Compared with several state-of-the-art algorithms, the proposed algorithm performs better.

Section: Improved Niching-based Cross-entropy Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A niching cross-entropy method for multimodal satellite layout optimization design

Sun

Chen

Zhang³

et al. 2021

“…The proposed algorithm could be adapted to learn non-linear metrics. Unal et al [59] created diversity in using Multi-Objective PSO (MOPSO) by using random immigrants approach. The application of the proposed solution is tested in four different sets using Generational Distance, Spacing, Error Ratio, and Run Time performance measures.…”

Section: Literature Surveymentioning

confidence: 99%

Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

Shaw

Ahmed

Malakar³

et al. 2021

Many real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

“…The performance of the improved algorithm was significantly improved. In addition, these algorithms can also be used to solve vehicle routing problems, such as particle swarm optimization algorithm [20][21][22][23], bee colony algorithm [24][25][26], whale optimization algorithm [27], Dijkstra algorithm [28], and so on.…”

Section: Introductionmentioning

confidence: 99%

A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

Gao

Wang

et al. 2021

In this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.