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A Hybrid TOPSIS-PR-GWO Approach for Multi-objective Process Parameter Optimization

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Abstract

Optimization is a necessary task in process engineering to ensure maximization of the overall efficiency of production systems. Metaheuristic algorithms have gained enormous attention in the last decade to solve complex multi-modal optimization problems. Hybridization of metaheuristics is an interesting and fruitful research area as it improves performance capabilities of the existing algorithms. In this paper, a novel hybridized multi-objective optimization approach called TOPSIS-PR-GWO is introduced by combining a popular multi-criteria decision-making technique called TOPSIS (technique for order of preference by similarity to ideal solution) with grey wolf optimizer (GWO) for parametric optimization of an abrasive jet machining process. The polynomial regression (PR) metamodels are treated as the inputs to this hybrid optimizer. The performance of TOPSIS-PR-GWO is validated against the conventionally adopted weighted sum multi-objective optimization (WSMO) approach, and it is revealed that for the considered machining process, TOPSIS-PR-GWO provides 2 to 321% better solutions than those derived using WSMO approach. Moreover, TOPSIS-PR-GWO is also less computationally intensive than WSMO with approximately 3 to 9% savings in computational time.

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Data Availability

All data generated or analyzed during this study are included in this published article (and its supplementary information files).

Abbreviations

AJM:

Abrasive jet machining

ALO:

Ant lion optimization

BA:

Bat algorithm

BK:

Bottom kerf

CoCoSo:

Combined compromise solution

CRADIS:

Compromise ranking of alternatives from distance to ideal solution

EEGWO:

Exploration-enhanced GWO

GA:

Genetic algorithm

GP:

Genetic programming

GWO:

Grey wolf optimizer

LGWO:

Lévy-embedded GWO

MABAC:

Multi-attributive border approximation area comparison

MARICA:

Multi-attributive real–ideal comparative analysis

MCDM:

Multi-criteria decision-making

MRR:

Material removal rate

PR:

Polynomial regression

PSO:

Particle swarm optimization

SQP:

Sequential quadratic programming

TK:

Top kerf

TLBO:

Teaching–learning-based optimization

TOPSIS:

Technique for order of preference by similarity to ideal solution

VWGWO:

GWO with variable weights

WOA:

Whale optimization algorithm

WSMO:

Weighted sum multi-objective optimization

\(\overrightarrow{a}\) :

A vector

\(\overrightarrow{A}\) :

Coefficient vector

A + :

Ideal solution in TOPSIS

A − :

Anti-ideal solution in TOPSIS

\(\overrightarrow{C}\) :

Coefficient vector

CC i :

Closeness coefficient of ith alternative

D :

Nozzle diameter

L :

Stand-off distance

m :

Number of alternatives

n :

Number of criteria (responses)

n ij :

Element of normalized decision matrix

P :

Pressure

\({\overrightarrow{r}}_{1}\) and \({\overrightarrow{r}}_{2}\) :

Random vectors

r ij :

Element of weighted normalized decision matrix

S :

Abrasive size

S i + :

Distance from the ideal solution

S i − :

Distance from the anti-deal solution

t :

Iteration number

t max :

Maximum iteration number

\({\overrightarrow{W}}_{\left(t\right)}\) :

Position vector of grey wolf at iteration t

\({\overrightarrow{W}}_{\left(t+1\right)}\) :

Position vector of grey wolf at iteration (t + 1)

\({\overrightarrow{W}}_{p\left(t\right)}\) :

Position vector of prey at iteration t

w j :

Weight of jth criterion

X :

Fitness function value

X max :

Maximum fitness function value

X min :

Minimum fitness function value

x ij :

Element of the initial decision matrix

α, β, δ and ω :

Grey wolves

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Authors and Affiliations

  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr, Sagunthala R&D Institute of Science and Technology, Avadi, India

    Kanak Kalita

  2. Department of Aerospace Engineering & Applied Mechanics, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India

    Subham Pal & Salil Haldar

  3. Department of Production Engineering, Jadavpur University, Kolkata, India

    Shankar Chakraborty

Authors
  1. Kanak Kalita
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  2. Subham Pal
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  3. Salil Haldar
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  4. Shankar Chakraborty
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Correspondence to Shankar Chakraborty.

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Appendix

Appendix

Table 3 Experimental data for AJM of glass fiber reinforced polymer composite (Madhu and Balasubramanian 2017)
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Kalita, K., Pal, S., Haldar, S. et al. A Hybrid TOPSIS-PR-GWO Approach for Multi-objective Process Parameter Optimization. Process Integr Optim Sustain 6, 1011–1026 (2022). https://doi.org/10.1007/s41660-022-00256-0

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