Modified teaching-learning-based optimization and applications in multi-response machining processes. (December 2022)

• Record Type: Journal Article
• Authors: Ang, Koon Meng; Natarajan, Elango; Mat Isa, Nor Ashidi; Sharma, Abhishek; Rahman, Hameedur; Then, Richie Yi Shiun; Alrifaey, Moath; Tiang, Sew Sun; Lim, Wei Hong
• Is Part Of: Computers & industrial engineering. Volume 174(2022)
• Publication Date: 2022-12
• Subjects: Multi-objective optimization -- Multi-response machining -- Pareto dominance -- Teaching-learning-based optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108719
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A new variant of multiobjective teaching–learning-based optimization is proposed. Good diversity preservation in modified teacher phase via unique search information. Improved learning efficiency in modified learner phase via two new search operators. The proposed algorithm can produce Pareto fronts with better quality than its peers. Abstract: Many real-world engineering problems such as machining processes are multi-objective optimization problems (MOPs) because multiple performance characteristics are considered to satisfy their contradictory goals. An improved multi-objective teaching–learning-based optimization with refined knowledge sharing mechanisms (IMTLBO-RKSM) is proposed to tackle these MOPs effectively. Pareto dominance concept is first incorporated into IMTLBO-RKSM to handle the tradeoffs of multiple contradictory objectives. Appropriate modifications are incorporated into both teacher and learner phases of IMTLBO-RKSM to emulate to emulate the knowledge sharing processes of classroom more accurately, hence achieving better balancing of exploration and exploitation searches. Particularly, both concepts of Euclidean-distance based teacher assignment scheme and social learning are incorporated into the IMTLBO-RKSM's teacher phase to derive the unique directional information that can provide better guidance for each learner. The learner phase of IMTLBO-RKSM is also modified by designing two new learning mechanisms known as independent learning and adaptive peer learning, aiming to facilitate different preferences of learners in acquiring new knowledge. The performance of IMTLBO-RKSM is evaluated and compared with six multi-objective optimization methods by using five case studies of multi-response machining problems and twelve MOP benchmark functions. Extensive simulation studies show that IMTLBO-RKSM have more competitive performance than other methods by generating Pareto fronts with better quality in terms of accuracy and diversity of solution members for most tested problems.