Tien-Szu Pan

Hybrid Particle Swarm Optimization with Bat Algorithm

In this paper, a communication strategy for hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

Parallel bat algorithm for optimizing makespan in job shop scheduling problems

Parallel processing plays an important role in efficient and effective computations of function optimization. In this paper, an optimization algorithm based on parallel versions of the bat algorithm (BA), random-key encoding scheme, communication strategy scheme and makespan scheme is proposed to solve the NP-hard job shop scheduling problem. The aim of the parallel BA with communication strategies is to correlate individuals in swarms and to share the computation load over few processors. Based on the original structure of the BA, the bat populations are split into several independent groups. In addition, the communication strategy provides the diversity-enhanced bats to speed up solutions. In the experiment, forty three instances of the benchmark in job shop scheduling data set with various sizes are used to test the behavior of the convergence, and accuracy of the proposed method. The results compared with the other methods in the literature show that the proposed scheme increases more the convergence and the accuracy than BA and particle swarm optimization.

Parallelized Bat Algorithm with a Communication Strategy

The trend in parallel processing is an essential requirement for optimum computations in modern equipment. In this paper, a communication strategy for the parallelized Bat Algorithm optimization is proposed for solving numerical optimization problems. The population bats are split into several independent groups based on the original structure of the Bat Algorithm BA, and the proposed communication strategy provides the information flow for the bats to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental result, the proposed communicational strategy increases the accuracy of the BA on finding the near best solution.

A Communication Strategy for Paralleling Grey Wolf Optimizer

In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.

A multi-objective optimal mobile robot path planning based on whale optimization algorithm

Due to the complex physical constraints in working space of robot, optimization for mobile robot operations satisfies not only one criterion but also several criteria. In this paper, a novel multi-objective method for optimal mobile robot path planning based on Whale optimization algorithm (WOA) is proposed. In the proposed method, two criteria of distance and smooth path of the robot path planning issue are transformed into a minimization one. The positions of the target and the obstacles in the environment are considered the fitness for the solution in WOA. The position of the globally best whale in each iteration is selected, and reached by the robot in sequence. In addition, the robot processor updates its information during the motion, and the environment is partially unknown for the robot due to the limit of the detection range of its sensors. Series of simulations are implemented in different static environments for the optimal path when the robot reaches its target. The results show that the proposed method provides the robot reaches its target with colliding free obstacles, and the proposed method may be the alternative method of optimization for robot planning.

Dynamic Diversity Population Based Flower Pollination Algorithm for Multimodal Optimization

Easy convergence to a local optimum, rather than global optimum could unexpectedly happen in practical multimodal optimization problems due to interference phenomena among physically constrained dimensions. In this paper, an altering strategy for dynamic diversity Flower pollination algorithm (FPA) is proposed for solving the multimodal optimization problems. In this proposed method, the population is divided into several small groups. Agents in these groups are exchanged frequently the evolved fitness information by using their own best historical information and the dynamic switching probability is to provide the diversity of searching process. A set of the benchmark functions is used to test the quality performance of the proposed method. The experimental result of the proposed method shows the better performance in comparison with others methods.

Evolved Bat Algorithm for Solving the Economic Load Dispatch Problem

Economic Load Dispatch (ELD) is one of the important optimization tasks, which provides an economic condition for the power systems. In this paper, Evolved Bat Algorithm (EBA) as an evolutionary based approach is presented to solve the constraint economic load dispatched problem of thermal plants. The output generating power for all the power-generation units can be determined by the optimal technique for transmission losses, power balance and generation capacity, so that the total constraint cost function is minimized. A piecewise quadratic function is used to show the fuel cost equation of each generation unit, and the B-coefficient matrix is used to represent transmission losses. The systems with six units and fifteen units of thermal plants are used to test the demonstration of the solution quality and computation efficiency of the feasibility of the application of the Evolved Bat Algorithm for ELD. The experimental results compared with the genetic algorithm (GA) method for ELD, and with the particle swarm optimization (PSO) method for ELD, show that the applied EBA method for ELD can provide the higher efficiency and accuracy.

Development of a Three-Dimensional Multimode Visual Immersive System With Applications in Telepresence

Three dimensional (3-D) visual immersive system provides a better experience of virtual or remote environments compared with 2-D vision and is thus becoming one of the most active research fields of virtual reality. This paper describes how a 3-D multimode visual immersive system (3DMVIS) is developed, including system architecture, triplet lens module, head orientation tracking circuit, display module, and sensor signal processing. It supports two operation modes for different applications, corresponding to different combinations of lens. As a portable head-mounted display (HMD) device, it can be used in extensive virtual reality and telepresence applications. In addition, this HMD provides visual accommodation with the aid of manual focus and compatibility with various types of 3-D videos. Hence, users with various levels of myopia can experience this facility without wearing glasses. Meanwhile, a typical application in telepresence is investigated along with a compact panoramic camera system. The 3DMVIS is tested and evaluated with various virtual reality scenes, standard-format 3-D movies, and real-world 360° panoramic videos. In addition, comparisons with other commercial counterparts are presented. Technical specification indicates that the 3DMVIS with its telepresence system satisfies the requirements of practical applications.

Compact Particle Swarm Optimization for Optimal Location of Base Station in Wireless Sensor Network

The computational requirements even in the limited resources of the hardware devices whose small memory size or low price could be addressed by compact optimization methods. In this paper, a compact particle swarm optimization (cPSO) for the base station locations optimization is proposed for wireless sensor networks (WSN). A probabilistic representation random of the collection behavior of swarms is inspired to employ for this proposed algorithm. The real population is replaced with the probability vector updated based on single competition. These lead to the entire algorithm functioning applying a modest memory usage. The experiments to solve the problem of locating the base station in WSN compared with the genetic algorithm (GA) method and the particle swarm optimization (PSO) method show that the proposed method can provide the effective way of using a modest memory.

A Compact Flower Pollination Algorithm Optimization

A restricted hardware condition is difficult for optimization problems. This paper proposes a novel compact flower pollination algorithm for addressing the class of optimization problems in the restricted hardware condition. In this proposed method, the actual population of tentative solutions is not stored, but a novel probabilistic representation on the population is employed based on the single competition. In the simulation, several problems of numerical optimizations in the benchmark are used to evaluate the accuracy, the computational time and the saving memory of the proposed method. The results compared with the original algorithm and the other algorithms in the literature show that the new proposed method provides the effective way of using a limited memory.