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multi-objective bonobo optimizer (MOBO): an intelligent heuristic for multi-criteria optimization

KNOWLEDGE AND INFORMATION SYSTEMS 2020年第11期62卷 4407-4444页

作者： Das, Amit Kumar Nikum, Ankit Kumar Krishnan, Siva Vignesh Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 W Bengal India SVNIT Dept Mech Engn Surat 395007 India

Non-traditional optimization tools have proved their potential in solving various types of optimization problems. These problems deal with either single objective or multiple/many objectives. bonobo optimizer (BO) is an intelligent and adaptive metaheuristic optimization algorithm inspired from the social behavior and reproductive strategies of bonobos. There is no study in the literature to extend this BO to solve multi-objective optimization problems. This paper presents a multi-objective bonobo optimizer (MOBO) to solve different optimization problems. Three different versions of MOBO are proposed in this paper, each using a different method, such as non-dominated sorting with adaptation of grid approach;a ranking scheme for sorting of population with crowding distance approach;decomposition technique, wherein the solutions are obtained by dividing a multi-objective problem into a number of single-objective problems. The performances of all three different versions of the proposed MOBO had been tested on a set of thirty diversified benchmark test functions, and the results were compared with that of four other well-known multi-objective optimization techniques available in the literature. The obtained results showed that the first two versions of the proposed algorithms either outperformed or performed competitively in terms of convergence and diversity compared to the others. However, the third version of the proposed techniques was found to have the poor performance.

关键词： multi-objective optimization bonobo optimizer Intelligent algorithm multi-objective bonobo optimizer multi-criteria optimization

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Parametric optimization of micro-tool fabrication through sheet-EDG using nature-inspired algorithms

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第2期46卷 1-17页

作者： Acharya, Biswesh Ranjan Sethi, Abhijeet Das, Amit Kumar Saha, Partha Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Kharagpur India Indian Inst Management Mumbai India Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur India

Sheet electrical discharge grinding (sheet-EDG) is a recently developed novel method for producing high aspect ratio micro-tools. However, due to the presence of varying nature of initial tool eccentricity for fabricating each micro-tool, determining the optimal process parameters for the mentioned process is a difficult task. To overcome the above issue, an alternative experimental investigation called the slit-cut process was conducted, in which tool rotation was not used. Face-centered central composite design-based response surface methodology (RSM) was conducted to produce 20 slit-cuts on sheet electrodes with varying input parameters like voltage, sheet-EDG parameter, and radial infeed. Later, these slit-cuts were assessed and responses like volume removal rate (VRR) and electrode wear rate (EWR) were evaluated. An analysis of variance (ANOVA) is conducted for both VRR and EWR and statistically validated regression equations were developed. Also, the effects of various input parameters on responses are discussed based on the main effect plots and surface plots. Both VRR and EWR were simultaneously optimized using desirability function approach and a variety of nature-inspired optimization algorithms, including the multi-objective bonobo optimizer (MOBO), multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimizer (MOGWO), and non-dominated sorting genetic algorithm (NSGA II). Based on the quality of the Pareto front, MOBO performed better than the other techniques. MOBO suggested changing the design ranges of voltage and radial feed from (140-180 V) to (140-156.8 V) and from (2-6 mu m/pass) to (5.2-6 mu m/pass), respectively.

关键词： Sheet electrical discharge grinding Micro-tool Non-dominated sorting genetic algorithm multi-objective particle swarm optimization multi-objective bonobo optimizer multi-objective grey wolf optimizer

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multi-objective optimization in electrochemical micro-drilling of Ti6Al4V using nature-inspired techniques

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MATERIALS AND MANUFACTURING PROCESSES 2023年第15期38卷 1942-1954页

作者： Acharya, Biswesh Ranjan Sethi, Abhijeet Das, Amit Kumar Saha, Partha Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur 721302 India Natl Inst Ind Engn NITIE Mumbai India

Current study investigates the efficacy of electrochemical micro-drilling (ECMD) process during micro-hole fabrication on a Ti6Al4V alloy sheet of 1.2 mm thickness using a cylindrical-shaped micro-tool. Ethylene glycol-based electrolyte was selected to overcome excessive passivation. Experiments were conducted by altering voltage, feed rate (FR), and electrolyte concentration (EC) using a central-composite-design to observe their effects on the output parameters. The desired response parameters, such as radial overcut (ROC), and taper were examined while fabricating dimensionally accurate micro-holes including the volume removal rate (VRR) that occurred during the process. ANOVA results established voltage as the major significant input parameter that affects all the responses, followed by FR. In addition, different nature-inspired optimization techniques, namely multi-objective Grey-Wolf optimizer (MOGWO), multi-objective bonobo optimizer (MOBO), and multi-objective Particle Swarm Optimization (MOPSO), were employed. MOBO reported the best Pareto-surface in terms of uniformity and spread measures and suggested a FR of 0.3 mu m/s for optimal micro-drilling operations.

关键词： Ti6Al4V Electrochemical micro-drilling multi-objective bonobo optimizer multi-objective particle swarm optimization multi-objective grey wolf optimizer Non-dominated sorting genetic algorithm

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