scholarly journals Manta ray foraging and Gaussian mutation-based elephant herding optimization for global optimization

2021 ◽  
Author(s):  
Yuxian Duan ◽  
Changyun Liu ◽  
Song Li ◽  
Xiangke Guo ◽  
Chunlin Yang
Keyword(s):  
Global Optimization ◽  
Foraging Strategy ◽  
Population Diversity ◽  
Gaussian Mutation ◽  
Running Time Analysis ◽  
Comparison Results ◽  
Engineering Problems ◽  
Solution Accuracy ◽  
Manta Ray ◽  
Search Capability

AbstractThe elephant herding optimization (EHO) algorithm is a novel metaheuristic optimizer inspired by the clan renewal and separation behaviors of elephant populations. Although it has few parameters and is easy to implement, it suffers from a lack of exploitation, leading to slow convergence. This paper proposes an improved EHO algorithm called manta ray foraging and Gaussian mutation-based EHO for global optimization (MGEHO). The clan updating operator in the original EHO algorithm is replaced by the somersault foraging strategy of manta rays, which aims to optimally adjust patriarch positions. Additionally, a dynamic convergence factor is set to balance exploration and exploitation. The gaussian mutation is adopted to enhance the population diversity, enabling MGEHO to maintain a strong local search capability. To evaluate the performances of different algorithms, 33 classical benchmark functions are chosen to verify the superiority of MGEHO. Also, the enhanced paradigm is compared with other advanced metaheuristic algorithms on 32 benchmark functions from IEEE CEC2014 and CEC2017. Furthermore, a scalability test, convergence analysis, statistical analysis, diversity analysis, and running time analysis demonstrate the effectiveness of MGEHO from various aspects. The results illustrate that MGEHO is superior to other algorithms in terms of solution accuracy and stability. Finally, MGEHO is applied to solve three real engineering problems. The comparison results show that this method is a powerful auxiliary tool for handling complex problems.

scholarly journals Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Abdulaziz S. Almazyad
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
High Dimensional ◽  
Gradual Change ◽  
Comparison Results ◽  
Mutation Strategy ◽  
Common Trade

This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

A Grunwald–Letnikov based Manta ray foraging optimizer for global optimization and image segmentation

2021 ◽  
Vol 98 ◽  
pp. 104105
Author(s):  
Mohamed Abd Elaziz ◽  
Dalia Yousri ◽  
Mohammed A.A. Al-qaness ◽  
Amr M. AbdelAty ◽  
Ahmed G. Radwan ◽  
...  
Keyword(s):  
Global Optimization ◽  
Image Segmentation ◽  
Manta Ray

scholarly journals An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1637
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Ali Fatahi ◽  
Hoda Zamani ◽  
Seyedali Mirjalili ◽  
Laith Abualigah
Keyword(s):  
Mechanical Engineering ◽  
Optimization Problems ◽  
Population Diversity ◽  
Local Optimum ◽  
Friedman Test ◽  
Exploration And Exploitation ◽  
Adaptation Mechanism ◽  
Local Optima ◽  
Engineering Problems ◽  
Moth Flame Optimization Algorithm

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

scholarly journals Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems

2020 ◽  
Vol 5 (3) ◽  
pp. 147-155
Author(s):  
I-Ming Chao ◽  
Shou-Cheng Hsiung ◽  
Jenn-Long Liu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Position Vector ◽  
Hunting Behavior ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Solution Accuracy

Whale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions.

A Levy Flight Sine Cosine Algorithm for Global Optimization Problems

2021 ◽  
Vol 12 (1) ◽  
pp. 49-66
Author(s):  
Yu Li ◽  
Yiran Zhao ◽  
Jingsen Liu
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Population Diversity ◽  
Lévy Flight ◽  
Simulation Experiments ◽  
Mathematical Functions ◽  
Levy Flight ◽  
Engineering Problems ◽  
Sine Cosine Algorithm ◽  
Swarm Intelligence Algorithm

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.

scholarly journals Neurogenetic Algorithm for Solving Combinatorial Engineering Problems

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
M. Jalali Varnamkhasti ◽  
Nasruddin Hassan
Keyword(s):  
Genetic Algorithm ◽  
Sexual Selection ◽  
Population Diversity ◽  
Nonlinear Functions ◽  
Inference System ◽  
Fitness Value ◽  
Gender And Age ◽  
Engineering Problems ◽  
Adaptive Neurofuzzy Inference System ◽  
Selection Of

Diversity of the population in a genetic algorithm plays an important role in impeding premature convergence. This paper proposes an adaptive neurofuzzy inference system genetic algorithm based on sexual selection. In this technique, for choosing the female chromosome during sexual selection, a bilinear allocation lifetime approach is used to label the chromosomes based on their fitness value which will then be used to characterize the diversity of the population. The motivation of this algorithm is to maintain the population diversity throughout the search procedure. To promote diversity, the proposed algorithm combines the concept of gender and age of individuals and the fuzzy logic during the selection of parents. In order to appraise the performance of the techniques used in this study, one of the chemistry problems and some nonlinear functions available in literature is used.

Catastrophe-Based Antibody Clone Algorithm

2011 ◽  
Vol 121-126 ◽  
pp. 4415-4420
Author(s):  
Yu Zhang ◽  
Li Hua Wu ◽  
Zi Qiang Luo
Keyword(s):  
Optimization Problems ◽  
Optimization Algorithms ◽  
Search Space ◽  
Natural World ◽  
Population Diversity ◽  
Selection Algorithm ◽  
Intelligent Optimization Algorithms ◽  
Complex Optimization ◽  
Search Capability ◽  
Better Than

In solving complex optimization problems, intelligent optimization algorithms such as immune algorithm show better advantages than traditional optimization algorithms. Most of these immune algorithms, however, have disadvantages in population diversity and preservation of elitist antibodies genes, which will lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. By introducing the catastrophe factor into the ACAMHC algorithm, we propose a novel catastrophe-based antibody clone algorithm (CACA) to solve the above problems. CACA preserves elitist antibody genes through the vaccine library to improve its local search capability; it improves the antibody population diversity by gene mutation that mimics the catastrophe events to the natural world to enhance its global search capability. To expand the antibody search space, CACA will add some new random immigrant antibodies with a certain ratio. The convergence of CACA is theoretically proved. The experiments of CACA compared with the clone selection algorithm (ACAMHC) on some benchmark functions are carried out. The experimental results indicate that the performance of CACA is better than that of ACAMHC. The CACA algorithm provides new opportunities for solving previously intractable optimization problems.

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