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We improve the approximated solution for the inventory model with a linear trend in demand such that there is no necessary to adjust the last or the first time interval. Our formula is easier to compute. We estimate our approximated result to the optimal solution of the equal time interval constraint, then our result is measured with an error of less than two-thirds. For the second inventory system examined in this paper, we consider an inventory system with two retailers and a manufacturer. Through the algebraic process, we obtain the optimal profit that is missing in the previously published paper. We show the elegant character of the algebraic process. [ABSTRACT FROM AUTHOR]

A new swarm-based metaheuristic, namely the enriched coati osprey algorithm (ECOA), is proposed in this paper. As its name suggests, ECOA hybridizes two new metaheuristics, the coati optimization algorithm (COA) and the osprey optimization algorithm (OOA). ECOA is constructed by five searches performed sequentially by the swarm members. The first three are directed searches, while the last two are neighborhood searches. All three directed searches are adopted from COA and OOA. Meanwhile, the four-bordered neighborhood search is developed based on a new approach. During the assessment, ECOA was challenged to overcome the set of 23 functions and contended with five new metaheuristics: total interaction algorithm (TIA), golden search optimization (GSO), average and subtraction-based optimization (ASBO), COA, and OOA. The result shows that ECOA outperforms TIA, GSO, ASBO, COA, and OOA in 16, 23, 18, 21, and 21 functions. Meanwhile, the individual search test result shows that the directed searches perform better than the neighborhood searches. Moreover, the directed search toward the best member becomes the most dominant search. [ABSTRACT FROM AUTHOR]

At present, artificial bee colony algorithm (ABC) is one of the hot issues in swarm intelligence algorithm. Since it was proposed, people have done a lot of improvement work for ABC algorithm. To address the shortcomings of ABC, an improved ABC guided by experience (named as EABC) is proposed in this paper. In EABC, it collects the experience of individual improvement caused by dimension change in the iterative process, and selects the dimensions to change according to a ratio when a new position needs to be generated. In this way, the individual can choose a good direction to improve its quality. Numerical experiments show that EABC has a better performance. [ABSTRACT FROM AUTHOR]

In this paper we present two path planning algorithms based on Bézier curves for autonomous vehicles with waypoints and corridor constraints. Bézier curves have useful properties for the path generation problem. This paper describes how the algorithms apply these properties to generate the reference trajectory for vehicles to satisfy the path constraints. Both algorithms join a set of low-degree Bézier curves segments smoothly to generate the path. Additionally, we discuss the constrained optimization problem that optimizes the resulting path for a user-defined cost function. The simulation demonstrates the improvement of trajectory generation in terms of smoother steering control and smaller cross track error compared to previous work. [ABSTRACT FROM AUTHOR]

A multilayer perceptron with backpropagation algorithm (BP) network that has the optimal number of neurons in its hidden layer would be able to predict accurately unknown values of a time series that it is trained with. A model known as K-means-Greedy Algorithm (KGA) model which combines greedy algorithm with k-means++ clustering is proposed in this paper to find the optimal number of neurons inside the hidden layer of the BP network. Experiments performed show that the proposed KGA model is effective in finding the optimal number of neurons for the hidden layer of a BP network that is used to perform prediction of unknown values of the Mackey-Glass time series. [ABSTRACT FROM AUTHOR]