Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

This paper proposes a novel optimization algorithm, called Emperor Penguin Optimizer (EPO), which mimics the huddling behavior of emperor penguins (Aptenodytes forsteri). The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions. It is compared with eight state-of-the-art optimization algorithms. The paper also considers for solving six real-life constrained and one unconstrained engineering design problems. The convergence and computational complexity are also analyzed to ensure the applicability of proposed algorithm. The experimental results show that the proposed algorithm is able to provide better results as compared to the other well-known metaheuristic algorithms.

Emperor penguin optimizer: A bio-inspired algorithm for engineering problems

A review: On path planning strategies for navigation of mobile robot

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

Chaotic Local Search-Based Differential Evolution Algorithms for Optimization

To cope with the potential shortcomings of classical fruit fly optimization algorithm (FOA), a new version of FOA with Gaussian mutation operator and the chaotic local search strategy (MCFOA) is proposed in this research. First, the Gaussian mutation operator is introduced into the basic FOA to avoid premature convergence and improve the exploitative tendencies in the algorithm (MFOA). Then, chaotic local search method is adopted for enhancing the local searching ability of the swarm of agents (CFOA). To substantiate the efficiency of three proposed methods, a comprehensive comparison has been completed using 23 benchmark functions with different characteristics. The best version of FOA among them is the MCFOA, which is extensively compared with the notable swarm-intelligence algorithms like bat algorithm (BA), particle swarm optimization algorithm (PSO), and several advanced FOA-based methods such as chaotic FOA (CIFOA), improved FOA (IFOA), multi-swarm FOA (swarm_MFOA) and differential evolution based FOA (DFOA). Numerical results show that two embedded strategies will effectively boost the performance of FOA for optimization tasks. In addition, MCFOA is also applied to feature selection problems. The results also prove that MCFOA can obtain the optimal classification accuracy.

Gaussian mutational chaotic fruit fly-built optimization and feature selection

Display Omitted Development of new method named chaotic fruit fly optimization algorithm (CFOA).Fruit fly algorithm (FOA) is integrated with ten different chaos maps.Novel algorithm is tested on ten different well known benchmark problems.CFOA is compared with FOA, FOA with Levy distribution, and similar chaotic methods.Experiments show superiority of CFOA in terms of obtained statistical results. Fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruit fly algorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruit fly algorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic firefly algorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate.

Chaotic fruit fly optimization algorithm

It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.

Neural network Reinforcement Learning for visual control of robot manipulators

Chaotic PSO algorithm is proposed to solve NP-hard IPPS problem.Ten chaotic maps are implemented to avoid premature convergence to local optimum.Makespan, balanced level of machine utilization and mean flow time are observed.Five experimental studies show that cPSO outperforms GA, SA, and hybrid algorithm.Scheduling plans are tested by mobile robot within a laboratory environment. Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

A variety of manufacturing operations together with a variety of alternative manufacturing resources provide that most jobs in the modern manufacturing systems may have a large number of alternative process plans. For that reason, obtaining an optimal process plan according to all alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) as well as alternative operations has become a very important task in flexible process planning problem research. In this paper, we present and evaluate a new algorithm for optimization of flexible process plans based on utilization of particle swarm optimization (PSO) algorithm and chaos theory. The main idea is to prevent the convergence of PSO in early stages of optimization process by implementing ten different chaotic maps which enlarge search space and provide its diversity. The flexible process plans are represented by using AND/OR network, and machine flexibility, tool flexibility, tool access direction (TAD) flexibility, process flexibility and sequence flexibility are considered. Further, mathematical models for minimization of production time and total production cost are derived. The newly developed algorithm is extensively experimentally verified by using four experimental studies, which show that the developed method outperforms genetic algorithm (GA), simulated annealing (SA), hybrid GA-SA and generic PSO based approach.

Chaotic particle swarm optimization algorithm for flexible process planning

Automated guided vehicles (AGVs) are a common choice made by many companies for material handling (MH) in manufacturing systems. AGV-based internal transport of raw materials, goods, and parts is becoming improved with advances in technology. Demands for fast, efficient, and reliable transport imply the usage of the flexible AGVs with onboard sensing and special kinds of algorithms needed for daily operations. So far, the majority of these transport solutions have not considered the modern techniques for visual servoing, monocular SLAM, and consequently, the usage of camera as onboard sensor for AGVs. In this research, a new hybrid control of AGV is proposed. The main control algorithm consists of two independent control loops: position-based control (PBC) for global navigation and image based visual seroving (IBVS) for fine motions needed for accurate steering towards loading/unloading point. By separating the initial transportation task into two parts (global navigation towards the goal pose near the loading/unloading point and fine motion from the goal pose to the loading/unloading point), the proposed hybrid control bypasses the need for artificial landmarks or accurate map of the environment. The state estimation of the robot pose is determined in terms of monocular SLAM, via extended Kalman filter coupled with feedforward neural network—the neural extended Kalman filter (NEKF). NEKF is used to model unknown disturbances and to improve the robot state transition model. The integration of the new hybrid control and NEKF has been tested in laboratory with the mobile robot and simple camera. Experimental results present the effectiveness of the proposed hybrid control approach.

Marko Mitić

Papers

Chaotic fruit fly optimization algorithm

Neural network Reinforcement Learning for visual control of robot manipulators

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Chaotic particle swarm optimization algorithm for flexible process planning

New hybrid vision-based control approach for automated guided vehicles