scholarly journals An enhanced chimp optimization algorithm for continuous optimization domains

2021 ◽  
Author(s):  
Heming Jia ◽  
Kangjian Sun ◽  
Wanying Zhang ◽  
Xin Leng
Keyword(s):  
Social Status ◽  
Optimization Algorithm ◽  
Continuous Optimization ◽  
Rank Correlation ◽  
Function Optimization ◽  
Local Optimum ◽  
Hunting Behavior ◽  
Spearman’S Rank Correlation ◽  
Engineering Design Problems ◽  
Application Fields

AbstractChimp optimization algorithm (ChOA) is a recently proposed metaheuristic. Interestingly, it simulates the social status relationship and hunting behavior of chimps. Due to the more flexible and complex application fields, researchers have higher requirements for native algorithms. In this paper, an enhanced chimp optimization algorithm (EChOA) is proposed to improve the accuracy of solutions. First, the highly disruptive polynomial mutation is used to initialize the population, which provides the foundation for global search. Next, Spearman’s rank correlation coefficient of the chimps with the lowest social status is calculated with respect to the leader chimp. To reduce the probability of falling into the local optimum, the beetle antennae operator is used to improve the less fit chimps while gaining visual capability. Three strategies enhance the exploration and exploitation of the native algorithm. To verify the function optimization performance, EChOA is comprehensively analyzed on 12 classical benchmark functions and 15 CEC2017 benchmark functions. Besides, the practicability of EChOA is also highlighted by three engineering design problems and training multilayer perceptron. Compared with ChOA and five state-of-the-art algorithms, the statistical results show that EChOA has strong competitive capabilities and promising prospects.

scholarly journals Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 282
Author(s):  
Di Wu ◽  
Wanying Zhang ◽  
Heming Jia ◽  
Xin Leng
Keyword(s):  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Search Algorithm ◽  
Rank Correlation ◽  
Population Diversity ◽  
The Other ◽  
Support Vector ◽  
Local Optimum ◽  
Original Algorithm ◽  
Spearman’S Rank Correlation

Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for the purpose of hunting. However, there are still some defects that lead to the algorithm falling into the local optimum. To overcome these defects, an Enhanced Chimp Optimization Algorithm (EChOA) is developed in this paper. Highly Disruptive Polynomial Mutation (HDPM) is introduced to further explore the population space and increase the population diversity. Then, the Spearman’s rank correlation coefficient between the chimps with the highest fitness and the lowest fitness is calculated. In order to avoid the local optimization, the chimps with low fitness values are introduced with Beetle Antenna Search Algorithm (BAS) to obtain visual ability. Through the introduction of the above three strategies, the ability of population exploration and exploitation is enhanced. On this basis, this paper proposes an EChOA-SVM model, which can optimize parameters while selecting the features. Thus, the maximum classification accuracy can be achieved with as few features as possible. To verify the effectiveness of the proposed method, the proposed method is compared with seven common methods, including the original algorithm. Seventeen benchmark datasets from the UCI machine learning library are used to evaluate the accuracy, number of features, and fitness of these methods. Experimental results show that the classification accuracy of the proposed method is better than the other methods on most data sets, and the number of features required by the proposed method is also less than the other algorithms.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

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 A New Optimization Algorithm Based on Search and Rescue Operations

2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Amir Shabani ◽  
Behrouz Asgarian ◽  
Saeed Asil Gharebaghi ◽  
Miguel A. Salido ◽  
Adriana Giret
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Search And Rescue ◽  
Rank Test ◽  
Engineering Design Problems ◽  
Continuous Optimization Problems

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization 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.

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.

MEMS Relay Optimization Design Algorithm Based on Particle Swarm Optimization

2013 ◽  
Vol 562-565 ◽  
pp. 155-161
Author(s):  
Han Min Liu ◽  
Qing Hua Wu ◽  
Xue Song Yan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Design ◽  
Particle Swarm ◽  
Nonlinear Function ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Design Algorithm ◽  
Nonlinear Function Optimization

Mathematical model of the MEMS relay volume involves in mechanical, electrical, magnetic, thermal, etc., the MEMS relay optimization design is a constrained nonlinear function optimization problem. In this paper, aim at the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, we improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solutions when compared to other heuristic methods and is a powerful optimization algorithm for MEMS relay optimization design.

scholarly journals Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Haorui Liu ◽  
Fengyan Yi ◽  
Heli Yang
Keyword(s):  
Quantitative Research ◽  
Optimization Problems ◽  
Cloud Model ◽  
Continuous Optimization ◽  
Convergence Speed ◽  
Function Optimization ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping ◽  
Continuous Optimization Problems

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion.

scholarly journals IWOA: An improved whale optimization algorithm for optimization problems

2019 ◽  
Vol 6 (3) ◽  
pp. 243-259 ◽  
Author(s):  
Seyed Mostafa Bozorgi ◽  
Samaneh Yazdani
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Function Optimization ◽  
Whale Optimization Algorithm ◽  
Exploration And Exploitation ◽  
Hunting Behavior ◽  
Local Optima ◽  
Adaptive Parameter ◽  
Whale Optimization

Abstract The whale optimization algorithm (WOA) is a new bio-inspired meta-heuristic algorithm which is presented based on the social hunting behavior of humpback whales. WOA suffers premature convergence that causes it to trap in local optima. In order to overcome this limitation of WOA, in this paper WOA is hybridized with differential evolution (DE) which has good exploration ability for function optimization problems. The proposed method is named Improved WOA (IWOA). The proposed method, combines exploitation of WOA with exploration of DE and therefore provides a promising candidate solution. In addition, IWOA+ is presented in this paper which is an extended form of IWOA. IWOA+ utilizes re-initialization and adaptive parameter which controls the whole search process to obtain better solutions. IWOA and IWOA+ are validated on a set of 25 benchmark functions, and they are compared with PSO, DE, BBO, DE/BBO, PSO/GSA, SCA, MFO and WOA. Furthermore, the effects of dimensionality and population size on the performance of our proposed algorithms are studied. The results demonstrate that IWOA and IWOA+ outperform the other algorithms in terms of quality of the final solution and convergence rate. Highlights The exploration ability of WOA is improved via hybridizing it with DE's mutation. A new adaptive strategy is utilized for balancing the exploration and exploitation abilities. Re-initialization is used to increase the diversity of population. Two improvements are presented for WOA through balancing its exploration and exploitation. The results show that the proposed algorithms can improve the performance of WOA significantly.

scholarly journals Stability of milking order in goat over a long period (short communication)

2004 ◽  
Vol 47 (2) ◽  
pp. 203-208
Author(s):  
M. T. Górecki ◽  
J. Wójtowski
Keyword(s):  
Body Weight ◽  
Social Status ◽  
Least Squares ◽  
Milk Yield ◽  
Short Communication ◽  
Least Squares Method ◽  
Rank Correlation ◽  
Milk Productivity ◽  
Long Period ◽  
Spearman’S Rank Correlation

Abstract. The aim of this paper was to assess if goat milking order was stabile over a long period and if milking order is influenced by horned ness, age, body weight, breed, social status and milk productivity. Milking order was studied in three periods: the second half of 1999, and the first and second halves of 2000. To verify the results statistically, a variance analysis (ANOVA), the least squares method and Spearman's rank correlation were used. The observed milking order of goats was non-random and very similar in the two consecutive years. Milk yield correlated with the milking order in two of the three periods (higher–yielding goats entered the milking parlour first). Social status as well as age affected milking order only in one period (dominant and younger animals came to the milking parlour earlier). In one period it was observed that body weight influenced the milking order at a marginally significant level (p = 0.0598). Heavier goats were milked before lighter ones. It can be suggested, that milk productivity is the strongest predictor of the animal position in the milking order. However, habits seems to influence milking order stronger than measurable traits. The own results were compared with other studies on milking order in cattle, sheep and goats.

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