scholarly journals MOCHIO: A Novel Multi-Objective Coronavirus Herd Immunity Optimization Algorithm for Solving Brushless Direct Current Wheel Motor Design Optimization Problem

2021 ◽  
Author(s):  
Kumar C ◽  
Magdalin Mary D ◽  
Gunasekar T
Keyword(s):  
Design Optimization ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Design Problem ◽  
Optimization Problem ◽  
Herd Immunity ◽  
Maximum Efficiency ◽  
Bldc Motor ◽  
Multi Objective ◽  
Motor Design

Abstract A prominent and realistic problem in the domain of magnetics is the optimal design of a brushless direct current (BLDC) motor. A key challenge is designing a BLDC motor such that it functions efficiently with a minimum cost of materials to achieve maximum efficiency. Recently, a novel optimization technique inspired by nature, called the Coronavirus Herd Immunity Optimizer (CHIO), is proposed. The inspiration for this technique derives from the idea of herd immunity as a way of combating the coronavirus pandemic. A variant of Coronavirus Herd Immunity Optimizer called Multi-Objective Coronavirus Herd Immunity Optimizer (MOCHIO) is proposed in this paper, and it has been directly used to optimize the BLDC motor design optimization problem. The BLDC motor design problem has two main objectives, such as minimization of the motor mass and maximization of the motor efficiency with five constraints and five design variables. First, MOCHIO is tested with benchmark functions and then applied to the BLDC motor design problem. In addition, the experimental results are compared with the multi-objective whale optimization algorithm (MOWOA), multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimizer (MOPSO), multi-objective moth flame optimizer (MOMFO), and multi-objective bat algorithm (MOBA) are presented to confirm the viability and dominance of the proposed MOCHIO algorithm.

scholarly journals Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem

2022 ◽  
Vol 70 (2) ◽  
pp. 2435-2452
Author(s):  
M. Premkumar ◽  
Pradeep Jangir ◽  
B. Santhosh Kumar ◽  
Mohammad A. Alqudah ◽  
Kottakkaran Sooppy Nisar
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Design Problem ◽  
Bldc Motor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Multi Objective ◽  
Motor Design

scholarly journals Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

2021 ◽  
Vol 9 (5) ◽  
pp. 478
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui ◽  
Ping Yang ◽  
Linke Chen
Keyword(s):  
Design Optimization ◽  
Optimization Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Robotic Fish ◽  
Multidisciplinary Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cfd Method ◽  
The Individual

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.

Hierarchical Arrangement of Characteristics in Product Design Optimization Problems for Deeper Insight Into Optimized Results

2004 ◽  
Author(s):  
Masataka Yoshimura ◽  
Masahiko Taniguchi ◽  
Kazuhiro Izui ◽  
Shinji Nishiwaki
Keyword(s):  
Design Optimization ◽  
Product Design ◽  
Optimum Design ◽  
Design Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimization Method ◽  
Hierarchical Level ◽  
Hierarchical Arrangement ◽  
Insight Into

This paper proposes a design optimization method for machine products that is based on the decomposition of performance characteristics, or alternatively, extraction of simpler characteristics, to accommodate the specific features or difficulties of a particular design problem. The optimization problem is expressed using hierarchical constructions of the decomposed and extracted characteristics and the optimizations are sequentially repeated, starting with groups of characteristics having conflicting characteristics at the lowest hierarchical level and proceeding to higher levels. The proposed method not only effectively enables achieving optimum design solutions, but also facilitates deeper insight into the design optimization results, and aids obtaining ideas for breakthroughs in the optimum solutions. An applied example is given to demonstrate the effectiveness of the proposed method.

Hybrid QPSO-NNIA2 Algorithm for Multi-Objective Optimization Problem

2019 ◽  
Vol 33 (08) ◽  
pp. 1959025
Author(s):  
Lan Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Nearest Neighbor ◽  
Superior Performance ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Evolution Algebra ◽  
Neighbor List

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

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