A Two-Warehouse Model for Deteriorating Items with Holding Cost under Particle Swarm Optimization

A deterministic inventory model has been developed for deteriorating items and Particle Swarm Optimization (PSO) having a ramp type demands with the effects of inflation with two-warehouse facilities. The owned warehouse (OW) has a fixed capacity of W units; the rented warehouse (RW) has unlimited capacity. Here, we assumed that the inventory holding cost in RW is higher than those in OW. Shortages in inventory are allowed and partially backlogged and Particle Swarm Optimization (PSO) it is assumed that the inventory deteriorates over time at a variable deterioration rate. The effect of inflation has also been considered for various costs associated with the inventory system and Particle Swarm Optimization (PSO). Numerical example is also used to study the behaviour of the model. Cost minimization technique is used to get the expressions for total cost and other parameters

PERFORMANCE ANALYSIS OF PSO-PD CONTROLLER IN CONTROLING THE RIGID GANTRY CRANE SYSTEM

Karya tulis ini membahas tentang algoritma particle swarm optimization (PSO) untuk mengoptimalkan penguat pengendali PD yang dinamakan pengendali PSO-PD. Efektivitas algoritma pengendali yang diusulkan diuji dengan menggunakan fungsi step dan dibandingkan dengan pengendali PD berbasis Zigler-Nichols (ZN-PD). Hasil simulasi yang didapatkan menunjukkan bahwa pengendali PSO-PD menghasilkan waktu naik dan waktu puncak yang lebih lambat dibandingkan dengan pengendali ZN-PD, tetapi memiliki waktu tunak yang lebih cepat dan nilai overshoot yang kecil di bawah trayektori yang didefinisikan.Kata kunci: Sistem gantry crane, PSO, Gain PD, Sudut ayunan AbstractThis paper presents the particle swarm optimization (PSO) algorithm to optimize the gains of the PD controller to form what so-called the particle swarm optimization (PSO-PD) controller. The effectiveness of the proposed control algorithm is tested under constant step function and compared with Ziegler-Nichols (ZN-PD) controller. Simulation results show that proposed controller has slower rise time and peak time than ZN-PD controller as well as small overshoot under the predefined trajectories

Novel Artificial Human Optimization Field Algorithms - The Beginning

New Artificial Human Optimization (AHO) Field Algorithms can be created from scratch or by adding the concept of Artificial Humans into other existing Optimization Algorithms. Particle Swarm Optimization (PSO) has been very popular for solving complex optimization problems due to its simplicity. In this work, new Artificial Human Optimization Field Algorithms are created by modifying existing PSO algorithms with AHO Field Concepts. These Hybrid PSO Algorithms comes under PSO Field as well as AHO Field. There are Hybrid PSO research articles based on Human Behavior, Human Cognition and Human Thinking etc. But there are no Hybrid PSO articles which based on concepts like Human Disease, Human Kindness and Human Relaxation. This paper proposes new AHO Field algorithms based on these research gaps. Some existing Hybrid PSO algorithms are given a new name in this work so that it will be easy for future AHO researchers to find these novel Artificial Human Optimization Field Algorithms. A total of 6 Artificial Human Optimization Field algorithms titled "Human Safety Particle Swarm Optimization (HuSaPSO)", "Human Kindness Particle Swarm Optimization (HKPSO)", "Human Relaxation Particle Swarm Optimization (HRPSO)", "Multiple Strategy Human Particle Swarm Optimization (MSHPSO)", "Human Thinking Particle Swarm Optimization (HTPSO)" and "Human Disease Particle Swarm Optimization (HDPSO)" are tested by applying these novel algorithms on Ackley, Beale, Bohachevsky, Booth and Three-Hump Camel Benchmark Functions. Results obtained are compared with PSO algorithm.Comment: 25 pages, 41 figure

Minimization of tool path length of drilling process using particle swarm optimization (PSO)

In the era of challenging economic, the industry in our country has been forced to produce a good quality product and increase the productivity of machining process simultaneously in order to compete with other countries. Drrilling process is one of a very important cutting process in industry. In a drilling for machining by Computer Numerical Control (CNC) such as drilling machines, the parameter of the tool routing path for the machining operation plays a very important role to minimize the machining time (Tiwari 2013, Rao and Kalyankar 2012) . This machine can be used with procedures for drilling, spreading, weaning and threading with a lot of the holes precisely. In order to increase the efficiency and productivity of drilling process, optimization on parameters of process can lead to better performance. Optimization of holes drilling operations will lead to reduction in time order and better productivity of manufacturing systems. Optimizing the tool path has played an important role, especially in mass production because reducing the time to produce one piece eventually lead to a significant reduction in the cost of the entire series (Pezer, 2016). In various publications and articles, scientists and researchers adapted several methods of artificial intelligence (AI) or hybrid optimization method for tool path artificial immune system (AIS), genetic algorithms (GA), Artificial Neural networks (ANN) Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) (Narooei and Ramli, 2014). These methods were been proven that can produce better performance and increase the productivity of drilling process. Therefore, in this study, the Particle Swarm Optimization (PSO) algorithm was develop in order to minimizing the tool path length in the drilling process which can produce the better results for the required machining time process. For this study, the main purpose is to apply the Particle Swarm Optimization (PSO) algorithm for use in searching for the optimal tool routing path for in simulation of drilling proces

Investigation of Conventional and AI Techniques for Online Application to Solve ELD, MED and CEED Problems

In this paper, one conventional and two AI techniques are investigated to find their suitability for ON-LINE application to solve Economic Load Dispatch (ELD), Minimum Emission Dispatch (MED) and Combined Economic and Emission Dispatch (CEED) problem. In this paper, three techniques,  Classical Lambda Iteration method, Particle Swarm Optimization (PSO) and Hopfield Neural Network (HNN) are applied to obtain  ELD, MED and CEED problem solutions for three, six and fifteen unit test systems. The results obtained show the superiority of HNN technique over the other two techniques. The solutions obtained are quite encouraging. The algorithm and simulations are carried out using MATLAB software. Keywords: ELD, MED, CEED, Conventional Lambda Technique, Particle Swarm Optimization (PSO), Hopfield Neural Network (HNN), Price Penalty Factor-PPF

Stowage Planning System for Ferry Ro-Ro Ships Using Particle Swarm Optimization Method

Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo.Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo

Comparison between the conventional methods and PSO based MPPT algorithm for photovoltaic systems

Since the output characteristics of photovoltaic (PV) system depends on the ambient temperature, solar radiation and load impedance, its maximum power point (MPP) is not constant. Under each condition PV module has a point at which it can produce its MPP. Therefore, a maximum power point tracking (MPPT) method is needed to uphold the PV panel operating at its MPP. This paper presents comparative study between the conventional MPPT methods used in (PV) system: Perturb and Observe (P&O), Incremental Conductance (IncCond), and Particle Swarm Optimization (PSO) algorithm for (MPPT) of (PV) system. To evaluate the study, the proposed PSO MPPT is implemented on a DC-DC cuk converter and has been compared with P&O and INcond methods in terms of their tracking speed, accuracy and performance by using the Matlab tool Simulink. The simulation result shows that the proposed algorithm is simple, and is superior to the P&O and IncCond methods

Firefly Algorithms for Multimodal Optimization

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research