Meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results

2022 ◽  
Author(s):  
Laith Abualigah ◽  
Mohamed Abd Elaziz ◽  
Ahmad M. Khasawneh ◽  
Mohammad Alshinwan ◽  
Rehab Ali Ibrahim ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Engineering Design ◽  
Real World ◽  
Mechanical Engineering ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Comprehensive Survey

scholarly journals Multipopulation Ensemble Particle Swarm Optimizer for Engineering Design Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-30
Author(s):  
Ziang Liu ◽  
Tatsushi Nishi
Keyword(s):  
Engineering Design ◽  
Real World ◽  
Particle Swarm ◽  
Engineering Problem ◽  
Particle Swarm Optimizer ◽  
Design Problems ◽  
Source Codes ◽  
Searching Strategy ◽  
Engineering Design Problems ◽  
Improved Function

Particle swarm optimization (PSO) is an efficient optimization algorithm and has been applied to solve various real-world problems. However, the performance of PSO on a specific problem highly depends on the velocity updating strategy. For a real-world engineering problem, the function landscapes are usually very complex and problem-specific knowledge is sometimes unavailable. To respond to this challenge, we propose a multipopulation ensemble particle swarm optimizer (MPEPSO). The proposed algorithm consists of three existing efficient and simple PSO searching strategies. The particles are divided into four subpopulations including three indicator subpopulations and one reward subpopulation. Particles in the three indicator subpopulations update their velocities by different strategies. During every learning period, the improved function values of the three strategies are recorded. At the end of a learning period, the reward subpopulation is allocated to the best-performed strategy. Therefore, the appropriate PSO searching strategy can have more computational expense. The performance of MPEPSO is evaluated by the CEC 2014 test suite and compared with six other efficient PSO variants. These results suggest that MPEPSO ranks the first among these algorithms. Moreover, MPEPSO is applied to solve four engineering design problems. The results show the advantages of MPEPSO. The MATLAB source codes of MPEPSO are available at https://github.com/zi-ang-liu/MPEPSO.

scholarly journals SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Mathematics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 250 ◽  
Author(s):  
Umesh Balande ◽  
Deepti Shrimankar
Keyword(s):  
Constrained Optimization ◽  
Engineering Design ◽  
Real World ◽  
Optimization Design ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Design Problems ◽  
Improved Firefly Algorithm ◽  
Engineering Design Problems ◽  
And Performance

Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

scholarly journals DEVELOPING A FRAMEWORK TO EVALUATE INDIVIDUAL LEARNING IN ENGINEERING DESIGN PROBLEMS – PART 1: TEACHING THROUGH SIMULACRA OF REAL-WORLD PROBLEMS

2019 ◽  
Author(s):  
Nishant Balakrishnan
Keyword(s):  
Engineering Design ◽  
Real World ◽  
Problem Based Learning ◽  
Design Problems ◽  
Design Engineers ◽  
Engineering Design Problems ◽  
University Of Manitoba ◽  
The University ◽  
Selection Of ◽  
Real World Problems

In the context of teaching design, engineers often have a strong preference for problem-based learning because the skills they are trying to teach are intrinsic to the solving of design problems. The proliferation of problem-based learning (PBL) in capstone and now cornerstone engineering design courses is well supported by industry and faculty and the trend has been towards seeing more PBL in engineering design courses. This paper explores the basic selection of engineering design problems and presents a fairly simple dilemma: the skills that are required to solve a problem are not necessarily the skills that are taught by the problem if the problem is truly open-ended. This paper presents the idea of using engineering problems that are carefully constructed simulacra of real-world problems with built in scaffolding to create PBL experiences for students that are educationally complete and meaningful. This paper presents examples from two courses developed at the University of Manitoba based on this approach, outcomes of and responses to the course layout, and ideas for how this model can be extended to other courses or programs.

Optimal Designs by Means of Genetic Algorithms

2021 ◽  
pp. 344-354
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Optimal Designs by Means of Genetic Algorithms

2018 ◽  
pp. 151-161
Author(s):  
Lata Nautiyal ◽  
Preeti Shivach ◽  
Mangey Ram
Keyword(s):  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Engineering Design ◽  
Design Problem ◽  
High Performance ◽  
Mechanical Engineering ◽  
Design Parameters ◽  
Design Problems ◽  
Simulation Tools ◽  
Engineering Design Problems

With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.

Tiki-taka algorithm: a novel metaheuristic inspired by football playing style

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohd Fadzil Faisae Ab. Rashid
Keyword(s):  
Engineering Design ◽  
Real World ◽  
Design Methodology ◽  
Benchmark Problems ◽  
Design Problems ◽  
System Complexity ◽  
Content Type ◽  
Engineering Design Problems ◽  
Real World Problems ◽  
Highly Cited

Purpose Metaheuristic algorithms have been commonly used as an optimisation tool in various fields. However, optimisation of real-world problems has become increasingly challenging with to increase in system complexity. This situation has become a pull factor to introduce an efficient metaheuristic. This study aims to propose a novel sport-inspired algorithm based on a football playing style called tiki-taka. Design/methodology/approach The tiki-taka football style is characterised by short passing, player positioning and maintaining possession. This style aims to dominate the ball possession and defeat opponents using its tactical superiority. The proposed tiki-taka algorithm (TTA) simulates the short passing and player positioning behaviour for optimisation. The algorithm was tested using 19 benchmark functions and five engineering design problems. The performance of the proposed algorithm was compared with 11 other metaheuristics from sport-based, highly cited and recent algorithms. Findings The results showed that the TTA is extremely competitive, ranking first and second on 84% of benchmark problems. The proposed algorithm performs best in two engineering design problems and ranks second in the three remaining problems. Originality/value The originality of the proposed algorithm is the short passing strategy that exploits a nearby player to move to a better position.

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