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"Designing Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure" by Mojtaba Sedighi and Fereydoon Rahnamay Roodposhti

Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model ....

Bombay Stock Exchange , Neuro Fuzzy Inference System , Particle Swarm Optimization , Genetic Algorithm , Stock Price Prediction , Technical Analysis ,

"A Self-adaptive Hybrid Search Technique with Its Application to the Qu" by Mehrdad Amirghasemi, Marcella Bernardo Papini et al.

Both the quadratic semi-assignment problem and the berth allocation problem are about assigning items (vessels) to sets (berths) and have various applications from floor layout planning to schedule synchronization in public transit networks and maritime logistics. In this paper, a hybrid, modular solution strategy, in which an adaptive improvement technique is embedded into a genetic algorithm, is proposed and has been applied to both problems. For the purpose of self-adaptivity, all important parameters of the procedure are embedded in the employed genomes and evolve while the procedure is executed. In addition to the hybrid strategy, a simple branch and bound brute force method is implemented to find the optimal solution for small instances. Computational experiments show that the presented procedure finds the optimal solution for randomly generated 20 × 5 instances in less than a millisecond. These instances are the largest QSAP instances for which we could find optimal solutions w ....

Berth Allocation , Genetic Algorithm , Maritime Logistics , Uadratic Semi Assignment ,