Design and Optimisation of Pressure Vessel Using Metaheuristic Approach

Hassan, Sulaiman; Kumar, K. Siva; Raj, Ch Deva; Sridhar, K.

doi:10.4028/www.scientific.net/amm.465-466.401

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…In this research, IT2FLC is used to check the stability for an inverted pendulum and compared the result with PID controller that proved IT2FLC is better than PID controller for this system. For future research, it will be of interest to study other types of real world problems such as impulsive effects on optimal controller [20], directional change error evaluation [21], water consumptions expenditure [22], solid steel beam at elevated [23] in the framework of [24] and type-2 fuzzy controller.…”

Section: Resultsmentioning

confidence: 99%

Intelligent System Stability using Type-2 Fuzzy Controller

Nagarajan

Lathamaheswari

Kavikumar

et al. 2019

IJIE

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To avoid mathematical complexity, Interval T2FSs (IT2FSs) have been pertained in majority of the fields. Type-2 fuzzy sets (T2FSs) handle a greater modeling and uncertainties that exist in the real world applications especially in control systems. One of the important components that influence the fuzzy controller is the triangular norm, which is the aggregation operator. For getting the stability of a control system T-norm operator can be preferred. Gaussian Interval Type-2 Membership Function (GIT2MF) has been used in this research. Mathematical properties of aggregation operator also proved using Gaussian Interval Type-2 Weighted Arithmetic (GIT2WA) operator. The aim of this research is analyzing the stability of an inverted pendulum using Interval Type-2 Fuzzy Logic Controller (IT2FLC) and the results are compared with traditional Proportional Integrated Derivative (PID) controller. It is observed that IT2FL controller gives better stability under imprecise condition.

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Section: Resultsmentioning

confidence: 99%

Intelligent System Stability using Type-2 Fuzzy Controller

Nagarajan

Lathamaheswari

Kavikumar

et al. 2019

IJIE

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“…The objective is to optimize manufacturing costs, which include material, forming, and welding costs (Hassan et al, 2014). The thickness of the shell is denoted by x 1 .…”

Section: Pressure Vesselmentioning

confidence: 99%

Lizard-Moth Flame Optimization Algorithm for Large-Scale Global Optimization Problems

Singh,

Sing,

Chaurasia

et al. 2024

Preprint

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This paper introduces a disruptive strategy, namely a lizard-hunting approach, into the classical Moth Flame Optimization (MFO) algorithm. The conventional MFO emulates a moth's navigation pattern around artificial light at night but tends to face stagnation due to the flame's exploitative tendencies, often getting trapped in local optima, particularly in higher-dimensional problems. The research motivation stems from the need to disrupt small groups stuck at various local optima after a certain number of iterations. To address the limitations of the existing MFO, the proposed Lizard-Moth-Flame Optimization (L-MFO) algorithm is put forth. In L-MFO, moth positions are classified into outlier and non-outlier categories using a clustering method in each iteration. Following this categorization, non-outliers are divided into highly and less densely populated subgroups, with the densely crowded group considered closer to the solution. However, a distinctive aspect of the lizard's behaviour in L-MFO is its inclination towards the less crowded group, reflecting a slower update. When a moth detects a lizard within its range and at the same angle, moths within the group either flee in the opposite direction or move towards a densely crowded group. This strategic response mitigates the issue of stagnation, enhancing the algorithm's overall performance. The proposed L-MFO algorithm undergoes a comprehensive evaluation by being compared with other state-of-the-art meta-heuristic algorithms. The assessment involves testing on twenty-three CEC-2005 benchmarks across different dimensions (10, 30, 50, 100, 500, 1000, 2000, and 5000), eight engineering problems, and 36 CEC-2017 benchmark functions with 10, 50 and 100 dimensions. The robustness of the algorithm is examined through convergence and divergence analysis, Wilcoxon signed-rank test, Two-tailed Mann-Whitney U test, and boxplot analysis. The experimental and statistical results consistently demonstrate the superior performance of L-MFO over other algorithms.

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“…In mechanical engineering, Muthu et al 82 conducted an optimal tolerance design for assembly with the goal of minimizing quality loss and manufacturing cost, using both GA and PSO algorithms. Hassan et al 83 applied the ACO algorithm to optimise pressure vessels' optimum design. Acharya et al 84 optimized proportional–integral–derivative control scheme parameters to regulate the DC motor’s speed.…”

Section: Related Workmentioning

confidence: 99%

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Azizi

Shishehgarkhaneh

Basiri

et al. 2023

Sci Rep

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In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of a traditional Korean game. Squid game is a multiplayer game with two primary objectives: attackers aim to complete their goal while teams try to eliminate each other, and it is usually played on large, open fields with no set guidelines for size and dimensions. The playfield for this game is often shaped like a squid and, according to historical context, appears to be around half the size of a standard basketball court. The mathematical model of this algorithm is developed based on a population of solution candidates with a random initialization process in the first stage. The solution candidates are divided into two groups of offensive and defensive players while the offensive player goes among the defensive players to start a fight which is modeled through a random movement toward the defensive players. By considering the winning states of the players of both sides which is calculated based on the objective function, the position updating process is conducted and the new position vectors are produced. To evaluate the effectiveness of the proposed SGO algorithm, 25 unconstrained mathematical test functions with 100 dimensions are used, alongside six other commonly used metaheuristics for comparison. 100 independent optimization runs are conducted for both SGO and the other algorithms with a pre-determined stopping condition to ensure statistical significance of the results. Statistical metrics such as mean, standard deviation, and mean of required objective function evaluations are calculated. To provide a more comprehensive analysis, four prominent statistical tests including the Kolmogorov–Smirnov, Mann–Whitney, and Kruskal–Wallis tests are used. Meanwhile, the ability of the suggested SGOA is assessed through the cutting-edge real-world problems on the newest CEC like CEC 2020, while the SGO demonstrate outstanding performance in dealing with these complex optimization problems. The overall assessment of the SGO indicates that the proposed algorithm can provide competitive and remarkable outcomes in both benchmark and real-world problems.

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Design and Optimisation of Pressure Vessel Using Metaheuristic Approach

Cited by 12 publications

References 4 publications

Intelligent System Stability using Type-2 Fuzzy Controller

Intelligent System Stability using Type-2 Fuzzy Controller

Lizard-Moth Flame Optimization Algorithm for Large-Scale Global Optimization Problems

Squid Game Optimizer (SGO): a novel metaheuristic algorithm

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