A Review on Advanced Optimization Algorithms in Multidisciplinary Applications

Sreedhar, Makesh Narsimhan; Reddy, S. Akshay Navaneeth; Chakra, S. Abhay; Kumar, TK Sandeep; Reddy, S. Sreenatha; Kumar, Bablu

doi:10.1007/978-981-15-1124-0_66

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…as given in Equation (26). Because rotation is a square orthonormal matrix, its inverse is simply its transpose.…”

Section: Bacterial Programmingmentioning

confidence: 99%

“…The most popular techniques are evolutionary computation, simulated annealing algorithms and swarm intelligence based techniques. Since these methods do not require gradient information, they are well suited for non-smooth or discontinuous optimization tasks occurring in nonlinear systems [26]. In this work a BP based kinematic chain estimation is proposed from INS based EKF angles.…”

Section: Introductionmentioning

confidence: 99%

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Bacterial Programming Based Kinematic Chain Estimation of Construction Vehicle

Pentek¹,

Botzheim²,

Czmerk³

2021

IEEE Access

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Construction vehicle automation for high accuracy applications require information about the state of the machine, resulting in a fully sensitized system with precise kinematic parameters. Since the measurement of these parameters contains uncertainties, accurate measurement of them is an expensive task. Automatic calibration of link parameters makes the task of kinematic parameter determination easier. This study reports a method for forward kinematic chain estimation of an excavator by bacterial programming (BP) based on randomly placed inertial navigation systems (INS) per segments with microelectromechanical sensors (MEMS) within. MEMS INS with fusion techniques provide increasing accuracy with outstanding resilience against harsh environment in a rigid housing. With known robot kinematic the tool orientation estimation can be made more accurate also the path can be planned. The unknown model structure and parameters are established and identified by BP without any a priori or given information about the device according to Denavit-Hartenberg (DH) transformation conventions. Fundamentals of this approach are described in detail and shown on simulated measurement results.INDEX TERMS Bacterial programming, inertial navigation system, Denavit-Hartenberg, MEMS, kinematics, extended Kalman filter.

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“…as given in Equation (26). Because rotation is a square orthonormal matrix, its inverse is simply its transpose.…”

Section: Bacterial Programmingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bacterial Programming Based Kinematic Chain Estimation of Construction Vehicle

Pentek¹,

Botzheim²,

Czmerk³

2021

IEEE Access

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“…Genetic algorithms were proposed as an alternative to solve problems that are difficult to solve in linear programming (Sreedhar, et al, 2020). In this work, a software tool was developed where genetic, heuristic, metaheuristic, and bioinspired algorithms were implemented and tested.…”

Section: Introductionmentioning

confidence: 99%

Application of the Real-Time Fan Scheduling in the Exploration-Exploitation to Optimize Minimum Functions Objectives

LARIOS,

M. QUINTERO-FLORES,

ANZURES-GARCÍA

et al. 2023

acs

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This paper presents the application of a task scheduling algorithm called Fan on an artificial intelligence technique as genetic algorithms for the problem of finding minima in objective functions, where the equations are predefined to measure the return on an investment. This work combines the methodologies of exploration and exploitation of a population, obtaining results with good aptitudes until finding a better learning based on conditions of not ending until an individual delivers a better aptitude, complying with the established restrictions, exhausting all possible options and fulfilling a stop condition. A real-time task planning algorithm was applied based on consensus techniques. A software tool was developed, and the scheduler called FAN was adapted that contemplates the execution of periodic, aperiodic, and sporadic tasks focused on controlled environments, considering that strict time restrictions are met. In the first phase of the work, it is shown how convergence precipitates to an evolution, this is done in few iterations. In a second stage, exploitation was improved, giving the algorithm a better performance in convergence and feasibility. As a result, there is the exploitation of the population and applying iterations with the fan algorithm and better aptitudes were obtained that occur through asynchronized processes under real-time planning concurrently.

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“…With the continuous innovation of technology and the rapid development of industrial production, the multiobjective optimization problem (MOP) has gradually become a research focus in the current scientific and engineering fields [1]. e subobjectives in a multiobjective optimization problem are usually mutually constrained.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Multiobjective Empire Split Algorithm

Liang

2020

Journal of Control Science and Engineering

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In the last two decades, swarm intelligence optimization algorithms have been widely studied and applied to multiobjective optimization problems. In multiobjective optimization, reproduction operations and the balance of convergence and diversity are two crucial issues. Imperialist competitive algorithm (ICA) and sine cosine algorithm (SCA) are two potential algorithms for handling single-objective optimization problems, but the research of them in multiobjective optimization is scarce. In this paper, a fusion multiobjective empire split algorithm (FMOESA) is proposed. First, an initialization operation based on opposition-based learning strategy is hired to generate a good initial population. A new reproduction of offspring is introduced, which combines ICA and SCA. Besides, a novel power evaluation mechanism is proposed to identify individual performance, which takes into account both convergence and diversity of population. Experimental studies on several benchmark problems show that FMOESA is competitive compared with the state-of-the-art algorithms. Given both good performance and nice properties, the proposed algorithm could be an alternative tool when dealing with multiobjective optimization problems.

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A Review on Advanced Optimization Algorithms in Multidisciplinary Applications

Cited by 8 publications

References 21 publications

Bacterial Programming Based Kinematic Chain Estimation of Construction Vehicle

Bacterial Programming Based Kinematic Chain Estimation of Construction Vehicle

Application of the Real-Time Fan Scheduling in the Exploration-Exploitation to Optimize Minimum Functions Objectives

A Fusion Multiobjective Empire Split Algorithm

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