scholarly journals A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Binglian Zhu ◽  
Wenyong Zhu ◽  
Zijuan Liu ◽  
Qingyan Duan ◽  
Long Cao
Keyword(s):  
Numerical Optimization ◽  
Early Stage ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Accurate Solution ◽  
Test Functions ◽  
Local Optimal Solution ◽  
Quantum Behavior ◽  
The Mean ◽  
Benchmark Test Functions

This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution.

Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

2011 ◽  
Vol 148-149 ◽  
pp. 134-137 ◽  
Author(s):  
Pei Wei Tsai ◽  
Jeng Shyang Pan ◽  
Bin Yih Liao ◽  
Ming Jer Tsai ◽  
Vaci Istanda
Keyword(s):  
Swarm Intelligence ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Computational Cost ◽  
Bat Algorithm ◽  
Experimental Results ◽  
Computational Time ◽  
Test Functions ◽  
Novel Algorithm

Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

An Improved DE Algorithm Based on the New Crossover Strategy

2015 ◽  
Vol 713-715 ◽  
pp. 1583-1588
Author(s):  
Cao Liang Liang ◽  
Wang Rui Rong ◽  
Liu Man Dan
Keyword(s):  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Standard Test ◽  
Local Solution ◽  
Population Diversity ◽  
Test Functions ◽  
Algorithm Optimization ◽  
De Algorithm ◽  
Local Optimal Solution ◽  
Evolution Algorithm

Differential Evolution Algorithm (DE) is fast and stable, but it’s easy to fall into the local optimal solution and the population diversity reduces fast in the later period. In order to improve the algorithm optimization and convergence capability, this paper proposes an improved DE algorithm based on the new crossover strategy (CMDE). As to the Crossover-factor is decided by the proportion of the variance and the evolution process in each generation, so it can follow the process of evolution and constantly change; the added operation of Second Mutation can improve the capacity of solving problem, which algorithm falls into the local solution easily. With four standard test functions, the results show that the CMDE algorithm is superior to DE in convergence speed, precise and stability of algorithm.

scholarly journals A novel animal migration algorithm for global numerical optimization

2016 ◽  
Vol 13 (1) ◽  
pp. 259-285 ◽  
Author(s):  
Qifang Luo ◽  
Mingzhi Ma ◽  
Yongquan Zhou
Keyword(s):  
Numerical Optimization ◽  
Cuckoo Search ◽  
Test Functions ◽  
Migration Process ◽  
Global Numerical Optimization ◽  
Bee Colony ◽  
Intelligent Optimization Algorithms ◽  
Animal Migration Optimization ◽  
Animal Migration ◽  
Benchmark Test Functions

Animal migration optimization (AMO) searches optimization solutions by migration process and updating process. In this paper, a novel migration process has been proposed to improve the exploration and exploitation ability of the animal migration optimization. Twenty-three typical benchmark test functions are applied to verify the effects of these improvements. The results show that the improved algorithm has faster convergence speed and higher convergence precision than the original animal migration optimization and other some intelligent optimization algorithms such as particle swarm optimization (PSO), cuckoo search (CS), firefly algorithm (FA), bat-inspired algorithm (BA) and artificial bee colony (ABC).

scholarly journals Novel Optimization Algorithm Inspired by Camel Traveling Behavior

2016 ◽  
Vol 12 (2) ◽  
pp. 167-177 ◽  
Author(s):  
Mohammed Ibrahim ◽  
Ramzy Ali
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Test Functions ◽  
Desert Environment ◽  
Benchmark Test ◽  
Benchmark Test Functions

This article presents a novel optimization algorithm inspired by camel traveling behavior that called Camel algorithm (CA). Camel is one of the extraordinary animals with many distinguish characters that allow it to withstand the severer desert environment. The Camel algorithm used to find the optimal solution for several different benchmark test functions. The results of CA and the results of GA and PSO algorithms are experimentally compared. The results indicate that the promising search ability of camel algorithm is useful, produce good results and outperform the others for different test functions.

scholarly journals Improved Quantum-Behaved Particle Swarm Algorithm Based on Levy Flight

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Song Zheng ◽  
Xinwei Zhou ◽  
Xiaoqing Zheng ◽  
Ming Ge
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Particle Swarm Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Comparison Results ◽  
Local Optimal Solution ◽  
Quantum Behavior ◽  
Swarm Algorithm

To improve convergence speed and search accuracy, this paper proposes an improved quantum-behaved particle swarm optimization algorithm based on Levy flight. The improved algorithm reduces the probability of a local optimal solution through Levy flight and enhances the accuracy of the later search through a postsearch strategy. During the search process, the probability of quantum behavior is retained and the directivity of the particles is strengthened. According to the simulation comparison results, the improved quantum-behaved particle swarm algorithm exhibits faster convergence speed and higher accuracy.

scholarly journals Chaotic Immune Symbiotic Organisms Search Algorithm for Solving Optimisation Problem

2018 ◽  
Vol 7 (3.15) ◽  
pp. 73 ◽  
Author(s):  
Mohamad Khairuzzaman Mohamad Zamani ◽  
Ismail Musirin ◽  
Saiful Izwan Suliman ◽  
Sharifah Azma Syed Mustaffa
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Optimal Solutions ◽  
Test Functions ◽  
High Quality ◽  
Symbiotic Organisms Search ◽  
Benchmark Test Functions ◽  
Symbiotic Organisms ◽  
Optimisation Techniques ◽  
Optimisation Algorithms

Achieving an optimal solution is very crucial while solving a problem. To achieve the optimality required, optimisation techniques can be implemented while solving the problem. The presence of classical optimisation techniques has enabled an optimal solution to be obtained. However, as the complexity of the optimisation problem increased, classical optimisation techniques faced difficulties in providing optimal solutions. Heuristics-based algorithms were introduced to counter the problem faced by classical optimisation techniques. Good performance of these heuristics-based algorithm has been implied through various implementation in solving optimisation problems. Despite the performance of these algorithms, the flaws of these algorithms hinder them from producing high-quality results. To mitigate the problem, this paper presents the development of Chaotic Immune Symbiotic Organisms Search algorithm which was inspired by the element of diversification as well as the increased capability of exploration. The performance of the proposed algorithm has been tested by solving several benchmark test functions. A comparative study was also conducted with respect to several other existing optimisation algorithms resulted in the superiority of the proposed algorithm in providing high-quality solutions.  

Common Benchmark Functions for Metaheuristic Evaluation: A Review

2017 ◽  
Vol 1 (4-2) ◽  
pp. 218 ◽  
Author(s):  
Kashif Hussain ◽  
Mohd Najib Mohd Salleh ◽  
Shi Cheng ◽  
Rashid Naseem
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Evaluation Criteria ◽  
Metaheuristic Algorithm ◽  
Test Functions ◽  
Test Bed ◽  
Benchmark Test Functions ◽  
Experimental Runs ◽  
Selection Of ◽  
Real World Problems

In literature, benchmark test functions have been used for evaluating performance of metaheuristic algorithms. Algorithms that perform well on a set of numerical optimization problems are considered as effective methods for solving real-world problems. Different researchers choose different set of functions with varying configurations, as there exists no standard or universally agreed test-bed. This makes hard for researchers to select functions that can truly gauge the robustness of a metaheuristic algorithm which is being proposed. This review paper is an attempt to provide researchers with commonly used experimental settings, including selection of test functions with different modalities, dimensions, the number of experimental runs, and evaluation criteria. Hence, the proposed list of functions, based on existing literature, can be handily employed as an effective test-bed for evaluating either a new or modified variant of any existing metaheuristic algorithm. For embedding more complexity in the problems, these functions can be shifted or rotated for enhanced robustness.

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

scholarly journals Patterns of Sequelae in Women with a History of Localized Breast Cancer: Results from the French VICAN Survey

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1161
Author(s):  
Lidia Delrieu ◽  
Liacine Bouaoun ◽  
Douae El Fatouhi ◽  
Elise Dumas ◽  
Anne-Deborah Bouhnik ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Latent Class ◽  
Early Stage ◽  
Current Analysis ◽  
Functional Pain ◽  
Cognitive Sequelae ◽  
History Of ◽  
The Mean ◽  
Study Participants ◽  
Changes Over Time

Breast cancer (BC) remains complex for women both physically and psychologically. The objectives of this study were to (1) assess the evolution of the main sequelae and treatment two and five years after diagnosis in women with early-stage breast cancer, (2) explore patterns of sequelae associated with given sociodemographic, clinical, and lifestyle factors. The current analysis was based on 654 localized BC patients enrolled in the French nationwide longitudinal survey “vie après cancer” VICAN (January–June 2010). Information about study participants was collected at enrollment, two and five years after diagnosis. Changes over time of the main sequelae were analyzed and latent class analysis was performed to identify patterns of sequelae related to BC five years after diagnosis. The mean age (±SD) of study participants at inclusion was 49.7 (±10.5) years old. Six main classes of sequelae were identified two years and five years post-diagnosis (functional, pain, esthetic, fatigue, psychological, and gynecological). A significant decrease was observed for fatigue (p = 0.03) and an increase in cognitive sequelae was reported (p = 0.03). Two latent classes were identified—functional and esthetic patterns. Substantial sequelae remain up to five years after BC diagnosis. Changes in patient care pathways are needed to identify BC patients at a high risk.

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