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.

scholarly journals An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

2021 ◽  
Vol 18 (6) ◽  
pp. 7076-7109
Author(s):  
Shuang Wang ◽  
◽  
Heming Jia ◽  
Qingxin Liu ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Space ◽  
Search Performance ◽  
Exploration And Exploitation ◽  
Design Problems ◽  
Local Optima ◽  
Opposition Based Learning ◽  
Engineering Design Problems

<abstract> <p>This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.</p> </abstract>

scholarly journals An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

2022 ◽  
Vol 19 (1) ◽  
pp. 473-512
Author(s):  
Rong Zheng ◽  
◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Population Diversity ◽  
Test Functions ◽  
Design Problems ◽  
Local Optima ◽  
Switching Mechanism ◽  
Engineering Design Problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

scholarly journals Elite Opposition-Based Cognitive Behavior Optimization Algorithm for Global Optimization

2019 ◽  
Vol 28 (2) ◽  
pp. 185-217 ◽  
Author(s):  
Shaoling Zhang ◽  
Yongquan Zhou ◽  
Qifang Luo
Keyword(s):  
Optimization Algorithm ◽  
Information Exchange ◽  
Superior Performance ◽  
Adjustment Process ◽  
Cognitive Behavior ◽  
Design Problems ◽  
Opposition Based Learning ◽  
Engineering Design Problems ◽  
Third Stage ◽  
Three Stages

Abstract This paper presents an elite opposition-based cognitive behavior optimization algorithm (ECOA). The traditional COA is divided into three stages: rough search, information exchange and share, and intelligent adjustment process. In this paper, we introduce the elite opposition-based learning in the third stage of COA, with a view to avoid the latter congestion as well as to enhance the convergence speed. ECOA is validated by 23 benchmark functions and three engineering design problems, and the experimental results have proven the superior performance of ECOA compared to other algorithms in the literature.

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.

OWMA: An improved self-regulatory woodpecker mating algorithm using opposition-based learning and allocation of local memory for solving optimization problems

2021 ◽  
Vol 40 (1) ◽  
pp. 919-946
Author(s):  
Morteza Karimzadeh Parizi ◽  
Farshid Keynia ◽  
Amid Khatibi bardsiri
Keyword(s):  
Optimization Problems ◽  
Classification Problem ◽  
Research Literature ◽  
Exploration And Exploitation ◽  
Mathematical Functions ◽  
Local Optima ◽  
Local Memory ◽  
Opposition Based Learning ◽  
Metaheuristic Methods ◽  
Two Phases

Success of metaheuristic algorithms depends on the efficient balance between of exploration and exploitation phases. Any optimization algorithm requires a combination of diverse exploration and proper exploitation to avoid local optima. This paper proposes a new improved version of the Woodpecker Mating Algorithm (WMA), based on opposition-based learning, known as the OWMA aiming to develop exploration and exploitation capacities and establish a simultaneous balance between these two phases. This improvement consists of three major mechanisms, the first of which is the new Distance Opposition-based Learning (DOBL) mechanism for improving exploration, diversity, and convergence. The second mechanism is the allocation of local memory of personal experiences of search agents for developing the exploitation capacity. The third mechanism is the use of a self-regulatory and dynamic method for setting the Hα parameter to improve the Running Away function (RA) performance. The ability of the proposed algorithm to solve 23 benchmark mathematical functions was evaluated and compared to that of a series of the latest and most popular metaheuristic methods reviewed in the research literature. The proposed algorithm is also used as a Multi-Layer Perceptron (MLP) neural network trainer to solve the classification problem on four biomedical datasets and three function approximation datasets. In addition, the OWMA algorithm was evaluated in five optimization problems constrained by the real world. The simulation results proved the superior and promising performance of the proposed algorithm in the majority of evaluations. The results prove the superiority and promising performance of the proposed algorithm in solving very complicated optimization problems.

scholarly journals An Improved Water Strider Algorithm for Optimal Design of Skeletal Structures

2020 ◽  
Author(s):  
Ali Kaveh ◽  
Majid Ilchi Ghazaan ◽  
Arash Asadi
Keyword(s):  
Structural Optimization ◽  
Optimization Problems ◽  
Initial Population ◽  
Learning Method ◽  
Water Strider ◽  
Mathematical Functions ◽  
Design Problems ◽  
Skeletal Structures ◽  
Opposition Based Learning ◽  
Engineering Design Problems

Water Strider Algorithm (WSA) is a new metaheuristic method that is inspired by the life cycle of water striders. This study attempts to enhance the performance of the WSA in order to improve solution accuracy, reliability, and convergence speed. The new method, called improved water strider algorithm (IWSA), is tested in benchmark mathematical functions and some structural optimization problems. In the proposed algorithm, the standard WSA is augmented by utilizing an opposition-based learning method for the initial population as well as a mutation technique borrowed from the genetic algorithm. By employing Generalized Space Transformation Search (GSTS) as an opposition-based learning method, more promising regions of the search space are explored; therefore, the precision of the results is enhanced. By adding a mutation to the WSA, the method is helped to escape from local optimums which is essential for engineering design problems as well as complex mathematical optimization problems. First, the viability of IWSA is demonstrated by optimizing benchmark mathematical functions, and then it is applied to three skeletal structures to investigate its efficiency in structural design problems. IWSA is compared to the standard WSA and some other state-of-the-art metaheuristic algorithms. The results show the competence and robustness of the IWSA as an optimization algorithm in mathematical functions as well as in the field of structural optimization.

scholarly journals DTSMA: Dominant Swarm with Adaptive T-distribution Mutation-based Slime Mould Algorithm

2022 ◽  
Vol 19 (3) ◽  
pp. 2240-2285
Author(s):  
Shihong Yin ◽  
◽  
Qifang Luo ◽  
Yanlian Du ◽  
Yongquan Zhou ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Robot Manipulator ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Design Problems ◽  
Slime Mould ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Inverse Kinematics Problem ◽  
T Distribution

<abstract> <p>The slime mould algorithm (SMA) is a metaheuristic algorithm recently proposed, which is inspired by the oscillations of slime mould. Similar to other algorithms, SMA also has some disadvantages such as insufficient balance between exploration and exploitation, and easy to fall into local optimum. This paper, an improved SMA based on dominant swarm with adaptive t-distribution mutation (DTSMA) is proposed. In DTSMA, the dominant swarm is used improved the SMA's convergence speed, and the adaptive t-distribution mutation balances is used enhanced the exploration and exploitation ability. In addition, a new exploitation mechanism is hybridized to increase the diversity of populations. The performances of DTSMA are verified on CEC2019 functions and eight engineering design problems. The results show that for the CEC2019 functions, the DTSMA performances are best; for the engineering problems, DTSMA obtains better results than SMA and many algorithms in the literature when the constraints are satisfied. Furthermore, DTSMA is used to solve the inverse kinematics problem for a 7-DOF robot manipulator. The overall results show that DTSMA has a strong optimization ability. Therefore, the DTSMA is a promising metaheuristic optimization for global optimization problems.</p> </abstract>

scholarly journals Hybridizing sine cosine algorithm with multi-orthogonal search strategy for engineering design problems

2017 ◽  
Vol 5 (2) ◽  
pp. 249-273 ◽  
Author(s):  
Rizk M. Rizk-Allah
Keyword(s):  
Engineering Design ◽  
Search Strategy ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Design Problems ◽  
Local Optima ◽  
Manufacturing Optimization ◽  
Engineering Design Problems ◽  
Orthogonal Search ◽  
Sine Cosine Algorithm

Abstract This paper presents a new algorithm based on hybridizing the sine cosine algorithm (SCA) with a multi-orthogonal search strategy (MOSS), named multi-orthogonal sine cosine algorithm (MOSCA), for solving engineering design problems. The proposed MOSCA integrates the advantages of the SCA and MOSS to eliminate SCA's disadvantages, like unbalanced exploitation and the trapping in local optima. The proposed MOSCA works in two stages, firstly, the SCA phase starts the search process to enhance exploration capability. Secondly, the MOSS phase starts its search from SCA found so far to boost the exploitation tendencies. In this regard, MOSS phase can assist SCA phase to search based on deeper exploration/exploitation patterns as an alternative. Therefore, the MOSCA can be more robust, statistically sound, and quickly convergent. The performance of the MOSCA algorithm is investigated by applying it on eighteen benchmark problems and four engineering design problems. The experimental results indicate that MOSCA is a promising algorithm and outperforms the other algorithms in most cases. Highlights MOSCA is presented to solve design and manufacturing optimization problems efficiently. MOSCA is based on two phases namely, sine cosine algorithm (SCA) and multi-orthogonal search strategy (MOSS). The integrated MOSCA enhances exploration tendency and exploitation capability. The MOSCA can be more robust, statistically sound, and quickly convergent. New approach produced successful results compared to the literature studies.

scholarly journals A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4613
Author(s):  
Shah Fahad ◽  
Shiyou Yang ◽  
Rehan Ali Khan ◽  
Shafiullah Khan ◽  
Shoaib Ahmed Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Continuous Variables ◽  
Design Problems ◽  
Swarm Optimization ◽  
Electromagnetic Design ◽  
Quantum Particle Swarm Optimization ◽  
Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

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.

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