A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

– Nature‐inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks., – The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature., – BA has been carefully implemented and carried out optimization for eight well‐known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms., – The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.

Bat algorithm: a novel approach for global engineering optimization

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

Meteorology today : An introduction to weather, Climate, and the environment

This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods.

Reptile Search Algorithm (RSA): A nature-inspired meta-heuristic optimizer

The whale optimization algorithm (WOA) has been shown to be powerful in searching for an optimal solution. This paper proposes an improvement to the whale optimization algorithm that is based on a Levy flight trajectory and called the Levy flight trajectory-based whale optimization algorithm (LWOA). The LWOA makes the WOA faster and more robust and avoids premature convergence. The Levy flight trajectory is helpful for increasing the diversity of the population against premature convergence and enhancing the capability of jumping out of local optimal optima. This method helps obtaining a better tradeoff between the exploration and exploitation of the WOA. The proposed algorithm is characterized by quick convergence and high precision, and it can effectively get rid of a local optimum. The LWOA is further compared with other well-known nature-inspired algorithms on 23 benchmarks and solving infinite impulse response model identification. The statistical results on the benchmark functions show that the LWOA can significantly outperform others on a majority of the benchmark functions, especially in solving an optimization problem that has high dimensionality. Additionally, the superior identification capability of the proposed algorithm is evident from the results obtained through the simulation study compared with other algorithms. All the results prove the superiority of the LWOA.

Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization

A discrete invasive weed optimization algorithm for solving traveling salesman problem

Elite opposition-based flower pollination algorithm

The capacitated vehicle routing problem (CVRP) is an NP-hard problem with both engineering and theoretical interests. In this paper, a hybrid bat algorithm with path relinking (HBA-PR) is proposed to solve CVRP. The HBA-PR is constructed based on the framework of the continuous bat algorithm, the greedy randomized adaptive search procedure (GRASP) and path relinking are effectively integrated into the bat algorithm. Moreover, in order to further improve the performance, the random subsequences and single-point local search are operated with certain loudness (a probability). In order to verify the effectiveness of our approach and its efficiency and compare with other existing methodologies, several classical CVRP instances from three classes of CVRP benchmarks are selected to test. Experimental results and comparisons show the HBA-PR is effective for solving CVRPs.

https://downloads.hindawi.com/journals/mpe/2013/392789.pdf

A Hybrid Bat Algorithm with Path Relinking for the Capacitated Vehicle Routing Problem

Feature selection (FS) is a classic and challenging optimization task in the field of machine learning and data mining. An equilibrium optimizer (EO) is a novel physics-based optimization algorithm; it was inspired by controlled volume mass balance models for estimating dynamic and equilibrium states. This article presents two binary equilibrium optimizer algorithm and for selecting the optimal feature subset for classification problems. The first algorithm maps the continuous EO into a discrete type using S-shaped and V-shaped transfer functions (BEO-S and BEO-V). The second algorithm is based on the position of the current optimal solution (target) and position vector (BEO-T). To verify the performance of the proposed algorithm, 19 well-known UCI datasets are tested and compared with other advanced FS methods. The experimental results show that among the proposed binary EO algorithms, BEO-V2 has the best comprehensive performance and has better performance than other state-of-the-art metaheuristic algorithms in terms of the performance measures.

/pdf/an-efficient-binary-equilibrium-optimizer-algorithm-for-414wwn6qbu.pdf

Qifang Luo

Papers

Lévy Flight Trajectory-Based Whale Optimization Algorithm for Global Optimization

A discrete invasive weed optimization algorithm for solving traveling salesman problem

Elite opposition-based flower pollination algorithm

A Hybrid Bat Algorithm with Path Relinking for the Capacitated Vehicle Routing Problem

An Efficient Binary Equilibrium Optimizer Algorithm for Feature Selection