scholarly journals A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Yuting Lu ◽  
Yongquan Zhou ◽  
Xiuli Wu
Keyword(s):  
Engineering Design ◽  
Simplex Method ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Function Optimization ◽  
Local Optimum ◽  
Computational Accuracy ◽  
Design Problems ◽  
Opposition Based Learning ◽  
Engineering Design Problems

In this paper, a novel hybrid lightning search algorithm-simplex method (LSA-SM) is proposed to solve the shortcomings of lightning search algorithm (LSA) premature convergence and low computational accuracy and it is applied to function optimization and constrained engineering design optimization problems. The improvement adds two major optimization strategies. Simplex method (SM) iteratively optimizes the current worst step leaders to avoid the population searching at the edge, thus improving the convergence accuracy and rate of the algorithm. Elite opposition-based learning (EOBL) increases the diversity of population to avoid the algorithm falling into local optimum. LSA-SM is tested by 18 benchmark functions and five constrained engineering design problems. The results show that LSA-SM has higher computational accuracy, faster convergence rate, and stronger stability than other algorithms and can effectively solve the problem of constrained nonlinear optimization in reality.

scholarly journals Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-22 ◽  
Author(s):  
Zhiming Li ◽  
Yongquan Zhou ◽  
Sen Zhang ◽  
Junmin Song
Keyword(s):  
Engineering Design ◽  
Function Optimization ◽  
Superior Performance ◽  
Local Optimum ◽  
Lévy Flight ◽  
Design Problems ◽  
Levy Flight ◽  
Spiral Path ◽  
Straight Line ◽  
Engineering Design Problems

The moth-flame optimization (MFO) algorithm is a novel nature-inspired heuristic paradigm. The main inspiration of this algorithm is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a spiral path around artificial lights. Aiming at the phenomenon that MFO algorithm has slow convergence and low precision, an improved version of MFO algorithm based on Lévy-flight strategy, which is named as LMFO, is proposed. Lévy-flight can increase the diversity of the population against premature convergence and make the algorithm jump out of local optimum more effectively. This approach is helpful to obtain a better trade-off between exploration and exploitation ability of MFO, thus, which can make LMFO faster and more robust than MFO. And a comparison with ABC, BA, GGSA, DA, PSOGSA, and MFO on 19 unconstrained benchmark functions and 2 constrained engineering design problems is tested. These results demonstrate the superior performance of LMFO.

scholarly journals Performance Comparison of Recent Population-Based Metaheuristic Optimisation Algorithms in Mechanical Design Problems of Machinery Components

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 341
Author(s):  
Bugra Alkan ◽  
Malarvizhi Kaniappan Chinnathai
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Computation Time ◽  
Population Based ◽  
Performance Comparison ◽  
Design Problems ◽  
Component Design ◽  
Engineering Design Problems ◽  
Optimisation Algorithms

The optimisation of complex engineering design problems is highly challenging due to the consideration of various design variables. To obtain acceptable near-optimal solutions within reasonable computation time, metaheuristics can be employed for such problems. However, a plethora of novel metaheuristic algorithms are developed and constantly improved and hence it is important to evaluate the applicability of the novel optimisation strategies and compare their performance using real-world engineering design problems. Therefore, in this paper, eight recent population-based metaheuristic optimisation algorithms—African Vultures Optimisation Algorithm (AVOA), Crystal Structure Algorithm (CryStAl), Human-Behaviour Based Optimisation (HBBO), Gradient-Based Optimiser (GBO), Gorilla Troops Optimiser (GTO), Runge–Kutta optimiser (RUN), Social Network Search (SNS) and Sparrow Search Algorithm (SSA)—are applied to five different mechanical component design problems and their performance on such problems are compared. The results show that the SNS algorithm is consistent, robust and provides better quality solutions at a relatively fast computation time for the considered design problems. GTO and GBO also show comparable performance across the considered problems and AVOA is the most efficient in terms of computation time.

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.

On combinatorial design spaces for the configuration design of product families

2001 ◽  
Vol 15 (2) ◽  
pp. 91-108 ◽  
Author(s):  
ZAHED SIDDIQUE ◽  
DAVID W. ROSEN
Keyword(s):  
Engineering Design ◽  
Design Space ◽  
Optimization Problems ◽  
Product Family ◽  
Product Variety ◽  
Combinatorial Design ◽  
Configuration Design ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Space Modeling

For typical optimization problems, the design space of interest is well defined: It is a subset of Rn, where n is the number of (continuous) variables. Constraints are often introduced to eliminate infeasible regions of this space from consideration. Many engineering design problems can be formulated as search in such a design space. For configuration design problems, however, the design space is much more difficult to define precisely, particularly when constraints are present. Configuration design spaces are discrete and combinatorial in nature, but not necessarily purely combinatorial, as certain combinations represent infeasible designs. One of our primary design objectives is to drastically reduce the effort to explore large combinatorial design spaces. We believe it is imperative to develop methods for mathematically defining design spaces for configuration design. The purpose of this paper is to outline our approach to defining configuration design spaces for engineering design, with an emphasis on the mathematics of the spaces and their combinations into larger spaces that more completely capture design requirements. Specifically, we introduce design spaces that model physical connectivity, functionality, and assemblability considerations for a representative product family, a class of coffeemakers. Then, we show how these spaces can be combined into a “common” product variety design space. We demonstrate how constraints can be defined and applied to these spaces so that feasible design regions can be directly modeled. Additionally, we explore the topological and combinatorial properties of these spaces. The application of this design space modeling methodology is illustrated using the coffeemaker product family.

An Effective Chaotic Cultural-Based Particle Swarm Optimization for Constrained Engineering Design Problems

2010 ◽  
Vol 20-23 ◽  
pp. 64-69 ◽  
Author(s):  
Yong Quan Zhou ◽  
Lingzi Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Engineering Design ◽  
Search Strategy ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Local Search Strategy

In this paper, a novel chaotic cultural-based particle swarm optimization algorithm (CCPSO) is proposed for constrained optimization problems by employing cultural-based particle swarm optimization (CPSO) algorithm and the notion of chaotic local search strategy. In the CCPSO, the shortcoming of cultural-based particle swarm optimization (CPSO) that it is easy to trap into local minimum be overcome, the chaotic local search strategy is introduced in the influence functions of cultural algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method.

scholarly journals An Improved Hybrid Aquila Optimizer and Harris Hawks Algorithm for Solving Industrial Engineering Optimization Problems

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1551
Author(s):  
Shuang Wang ◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
Rong Zheng
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
Energy Parameter ◽  
Industrial Engineering ◽  
Superior Performance ◽  
Exploration And Exploitation ◽  
Design Problems ◽  
Local Optima ◽  
Opposition Based Learning ◽  
Engineering Design Problems

Aquila Optimizer (AO) and Harris Hawks Optimizer (HHO) are recently proposed meta-heuristic optimization algorithms. AO possesses strong global exploration capability but insufficient local exploitation ability. However, the exploitation phase of HHO is pretty good, while the exploration capability is far from satisfactory. Considering the characteristics of these two algorithms, an improved hybrid AO and HHO combined with a nonlinear escaping energy parameter and random opposition-based learning strategy is proposed, namely IHAOHHO, to improve the searching performance in this paper. Firstly, combining the salient features of AO and HHO retains valuable exploration and exploitation capabilities. In the second place, random opposition-based learning (ROBL) is added in the exploitation phase to improve local optima avoidance. Finally, the nonlinear escaping energy parameter is utilized better to balance the exploration and exploitation phases of IHAOHHO. These two strategies effectively enhance the exploration and exploitation of the proposed algorithm. To verify the optimization performance, IHAOHHO is comprehensively analyzed on 23 standard benchmark functions. Moreover, the practicability of IHAOHHO is also highlighted by four industrial engineering design problems. Compared with the original AO and HHO and five state-of-the-art algorithms, the results show that IHAOHHO has strong superior performance and promising prospects.

Hybrid improved slime mould algorithm with adaptive β hill climbing for numerical optimization

2021 ◽  
Vol 40 (1) ◽  
pp. 1667-1679
Author(s):  
Kangjian Sun ◽  
Heming Jia ◽  
Yao Li ◽  
Zichao Jiang
Keyword(s):  
Search Algorithm ◽  
Computation Time ◽  
Function Optimization ◽  
Hill Climbing ◽  
Multilayer Perceptrons ◽  
Design Problems ◽  
Slime Mould ◽  
Wilcoxon Rank Sum Test ◽  
Comparison Results ◽  
Engineering Design Problems

Slime mould algorithm (SMA) is a novel metaheuristic that simulates foraging behavior of slime mould. Regarding its drawbacks and properties, a hybrid optimization (BTβSMA) based on improved SMA is proposed to produce the higher-quality optimal results. Brownian motion and tournament selection mechanism are introduced into the basic SMA to improve the exploration capability. Moreover, a local search algorithm (Adaptive β-hill climbing, AβHC) is hybridized with the improved SMA. It is considered from boosting the exploitation trend. The proposed BTβSMA algorithm is evaluated in two main phases. Firstly, the two improved hybrid variants (BTβSMA-1 and BTβSMA-2) are compared with the basic SMA algorithm through 16 benchmark functions. Also, the performance of winner is further evaluated through comparisons with 7 state-of-the-art algorithms. The simulation results report fitness and computation time. The convergence curve and boxplot visualize the effects of fitness. The comparison results on the function optimization suggest that BTβSMA is superior to competitors. Wilcoxon rank-sum test is also employed to investigate the significance of the results. Secondly, the applicability on real-world tasks is proved by solving structure engineering design problems and training multilayer perceptrons. The numerical results indicate the merits of the BTβSMA algorithm in terms of solution precision.

scholarly journals A Hybrid Pathfinder Optimizer for Unconstrained and Constrained Optimization Problems

2020 ◽  
Vol 2020 ◽  
pp. 1-25
Author(s):  
Xiangbo Qi ◽  
Zhonghu Yuan ◽  
Yan Song
Keyword(s):  
Engineering Design ◽  
Optimization Problems ◽  
Continuous Functions ◽  
The Other ◽  
Constrained Optimization Problems ◽  
Design Problems ◽  
Constrained Problems ◽  
Engineering Design Problems ◽  
Comparison Algorithms ◽  
Searching Ability

Hybridization of metaheuristic algorithms with local search has been investigated in many studies. This paper proposes a hybrid pathfinder algorithm (HPFA), which incorporates the mutation operator in differential evolution (DE) into the pathfinder algorithm (PFA). The proposed algorithm combines the searching ability of both PFA and DE. With a test on a set of twenty-four unconstrained benchmark functions including both unimodal continuous functions, multimodal continuous functions, and composition functions, HPFA is proved to have significant improvement over the pathfinder algorithm and the other comparison algorithms. Then HPFA is used for data clustering, constrained problems, and engineering design problems. The experimental results show that the proposed HPFA got better results than the other comparison algorithms and is a competitive approach for solving partitioning clustering, constrained problems, and engineering design problems.

scholarly journals An Improved Butterfly Optimization Algorithm for Engineering Design Problems Using the Cross-Entropy Method

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1049 ◽  
Author(s):  
Guocheng Li ◽  
Fei Shuang ◽  
Pan Zhao ◽  
Chengyi Le
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Global Search ◽  
Cross Entropy ◽  
Local Optimum ◽  
Test Functions ◽  
Design Problems ◽  
Cross Entropy Method ◽  
Engineering Design Problems ◽  
The Cross

Engineering design optimization in real life is a challenging global optimization problem, and many meta-heuristic algorithms have been proposed to obtain the global best solutions. An excellent meta-heuristic algorithm has two symmetric search capabilities: local search and global search. In this paper, an improved Butterfly Optimization Algorithm (BOA) is developed by embedding the cross-entropy (CE) method into the original BOA. Based on a co-evolution technique, this new method achieves a proper balance between exploration and exploitation to enhance its global search capability, and effectively avoid it falling into a local optimum. The performance of the proposed approach was evaluated on 19 well-known benchmark test functions and three classical engineering design problems. The results of the test functions show that the proposed algorithm can provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence rate. The results of the engineering problems prove that the new approach is applicable to challenging problems with constrained and unknown search spaces.

Hybrid Weights-Utility and Taguchi Method for Multi-Objective Optimization Problems

2015 ◽  
Vol 764-765 ◽  
pp. 305-308
Author(s):  
Kuang Hung Hsien ◽  
Shyh Chour Huang
Keyword(s):  
Taguchi Method ◽  
Engineering Design ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Design Problems ◽  
Multi Objective ◽  
Utility Concept ◽  
Engineering Design Problems

In this paper, hybrid weights-utility and Taguchi method is proposed to solve multi-objective optimization problems. The new method combines the Taguchi method and the weights-utility concept. The weights of the objective function and overall utility values are very important for the weights-utility, and must be set correctly in order to obtain an optimal solution. Application of this method to engineering design problems is illustrated with the aid of one case study, and the result shows that the weights-utlity method is able to handle multi-objective optimization problems, with an optimal solution which better meets the demand of multi-objective optimization problems than the utility concept does.

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