scholarly journals Viewing the Problem from Different Angles: A New Diversity Measure Based on Angular Distances

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Henrik Berg
Keyword(s):  
Population Diversity ◽  
Evolutionary System ◽  
Local Optima ◽  
Diversity Measure ◽  
High Diversity

It is commonly believed that diversity is crucial for an evolutionary system to succeed, especially when the problem to be solved contains local optima from which the population cannot easily escape. There exist numerous methods to measure population diversity, but none of these have been shown to be consistently useful. In this paper, a new diversity measure is introduced, and it is shown that high diversity according to this new measure generally leads to a more successful overall evolution in most of the cases considered.

Improved Hybrid Particle Swarm Optimizer With Sine-Cosine Acceleration Coefficients For Transient Electromagnetic Inversion

2021 ◽  
Vol 16 ◽  
Author(s):  
Ruiheng Li ◽  
Qiong Zhuang ◽  
Nian Yu ◽  
Ruiyou Li ◽  
Huaiqing Zhang
Keyword(s):  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Nonlinear Inversion ◽  
Test Functions ◽  
Particle Swarm Optimizer ◽  
Local Optima ◽  
Hybrid Particle ◽  
Transient Electromagnetic ◽  
Stability And Accuracy

Background: Recently, particle swarm optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence. Objective: However, PSO lacks population diversity and may fall to local optima. Hence, an improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions and in transient electromagnetic (TEM) nonlinear inversion. Method: A reverse learning strategy is applied to optimize population initialization. The sine-cosine acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population diversity during the search process. In addition, the mutation method is used to reduce the probability of premature convergence. Results: The application of IH-PSO-SCAC in the test functions and several simple layered models are demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test our algorithm. The first model contains an underground low-resistivity anomaly body and the second model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases, resistivity profiles are obtained, and the inverse problem is solved for verification. Conclusion: The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.

A Guided Mutation Operator for Dynamic Diversity Enhancement in Evolutionary Strategies

2014 ◽  
Vol 4 (2) ◽  
pp. 20-39
Author(s):  
José L. Guerrero ◽  
Antonio Berlanga ◽  
José M. Molina
Keyword(s):  
Search Space ◽  
Critical Issue ◽  
Population Diversity ◽  
Evolutionary Strategies ◽  
Mutation Operator ◽  
Local Optima ◽  
Guided Mutation ◽  
Statistical Relevance ◽  
Number Of Cycles ◽  
Diversity Enhancement

Diversity in evolutionary algorithms is a critical issue related to the performance obtained during the search process and strongly linked to convergence issues. The lack of the required diversity has been traditionally linked to problematic situations such as early stopping in the presence of local optima (usually faced when the number of individuals in the population is insufficient to deal with the search space). Current proposal introduces a guided mutation operator to cope with these diversity issues, introducing tracking mechanisms of the search space in order to feed the required information to this mutation operator. The objective of the proposed mutation operator is to guarantee a certain degree of coverage over the search space before the algorithm is stopped, attempting to prevent early convergence, which may be introduced by the lack of population diversity. A dynamic mechanism is included in order to determine, in execution time, the degree of application of the technique, adapting the number of cycles when the technique is applied. The results have been tested over a dataset of ten standard single objective functions with different characteristics regarding dimensionality, presence of multiple local optima, search space range and three different dimensionality values, 30D, 300D and 1000D. Thirty different runs have been performed in order to cover the effect of the introduced operator and the statistical relevance of the measured results

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>

Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

2018 ◽  
pp. 27-52 ◽  
Author(s):  
Jiarui Zhou ◽  
Junshan Yang ◽  
Ling Lin ◽  
Zexuan Zhu ◽  
Zhen Ji
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Particle Swarm ◽  
Global Optimum ◽  
Population Diversity ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optima ◽  
Von Neumann ◽  
Swarm Intelligence Algorithm

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. It is easy to get trapped in local optima. For this reason, improvements are made to detect stagnation during the optimization and reactivate the swarm to search towards the global optimum. This chapter imposes the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel crown jewel defense (CJD) strategy is introduced to restart the swarm when it is trapped in a local optimum region. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting. Experimental results suggest that the LCJDPSO-rfl outperforms state-of-the-art PSO variants on most of the functions.

A Strength Pareto Gravitational Search Algorithm for Multi-Objective Optimization Problems

2015 ◽  
Vol 29 (06) ◽  
pp. 1559010 ◽  
Author(s):  
Xiaohui Yuan ◽  
Zhihuan Chen ◽  
Yanbin Yuan ◽  
Yuehua Huang ◽  
Xiaopan Zhang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Population Diversity ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Local Optima ◽  
Multi Objective ◽  
Fine Grained ◽  
Gravitational Search

A novel strength Pareto gravitational search algorithm (SPGSA) is proposed to solve multi-objective optimization problems. This SPGSA algorithm utilizes the strength Pareto concept to assign the fitness values for agents and uses a fine-grained elitism selection mechanism to keep the population diversity. Furthermore, the recombination operators are modeled in this approach to decrease the possibility of trapping in local optima. Experiments are conducted on a series of benchmark problems that are characterized by difficulties in local optimality, nonuniformity, and nonconvexity. The results show that the proposed SPGSA algorithm performs better in comparison with other related works. On the other hand, the effectiveness of two subtle means added to the GSA are verified, i.e. the fine-grained elitism selection and the use of SBX and PMO operators. Simulation results show that these measures not only improve the convergence ability of original GSA, but also preserve the population diversity adequately, which enables the SPGSA algorithm to have an excellent ability that keeps a desirable balance between the exploitation and exploration so as to accelerate the convergence speed to the true Pareto-optimal front.

scholarly journals A Novel Parent Centric Crossover with the Log-Logistic Probabilistic Approach Using Multimodal Test Problems for Real-Coded Genetic Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Ehtasham ul Haq ◽  
Ishfaq Ahmad ◽  
Ibrahim M. Almanjahie
Keyword(s):  
Optimization Problems ◽  
Probabilistic Approach ◽  
Population Diversity ◽  
Test Problems ◽  
Crossover Operator ◽  
Local Optima ◽  
Mutation Operators ◽  
Multiple Comparison Test ◽  
Power Mutation ◽  
Real Coded Genetic Algorithms

In this paper, a comprehensive empirical study is conducted to evaluate the performance of a new real-coded crossover operator called Fisk crossover (FX) operator. The basic aim of the proposed study is to preserve population diversity as well as to avoid local optima. In this context, a new crossover operator is designed and developed which is linked with Log-logistic probability distribution. For its global performance, a realistic comparison is made between FX versus double Pareto crossover (DPX), Laplace crossover (LX), and simulated binary crossover (SBX) operators. Moreover, these crossover operators are also used in conjunction with three mutation operators called power mutation (PM), Makinen, Periaux, and Toivanen mutation (MPTM), and nonuniform mutation (NUM) for inclusive evaluation. The performance of probabilistic-based algorithms is tested on a set of twenty-one well-known nonlinear optimization benchmark functions with diverse features. The empirical results show a substantial dominance of FX over other crossover operators with authentication of performance index (PI). Moreover, we also examined the significance of the proposed crossover scheme by administrating ANOVA and Gabriel pairwise multiple comparison test. Finally, the statistically significant results of the proposed crossover scheme have a definite edge over the other schemes, and it is also expected that FX has a great potential to solve complex optimization problems.

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 Stochastic Multiple Chaotic Local Search-Incorporated Gradient-Based Optimizer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hang Yu ◽  
Yu Zhang ◽  
Pengxing Cai ◽  
Junyan Yi ◽  
Sheng Li ◽  
...  
Keyword(s):  
Local Search ◽  
Optimization Problem ◽  
Search Strategy ◽  
State Of The Art ◽  
Population Diversity ◽  
Local Optima ◽  
Gradient Based ◽  
High Level ◽  
Search Rule ◽  
Better Than

In this study, a hybrid metaheuristic algorithm chaotic gradient-based optimizer (CGBO) is proposed. The gradient-based optimizer (GBO) is a novel metaheuristic inspired by Newton’s method which has two search strategies to ensure excellent performance. One is the gradient search rule (GSR), and the other is local escaping operation (LEO). GSR utilizes the gradient method to enhance ability of exploitation and convergence rate, and LEO employs random operators to escape the local optima. It is verified that gradient-based metaheuristic algorithms have obvious shortcomings in exploration. Meanwhile, chaotic local search (CLS) is an efficient search strategy with randomicity and ergodicity, which is usually used to improve global optimization algorithms. Accordingly, we incorporate GBO with CLS to strengthen the ability of exploration and keep high-level population diversity for original GBO. In this study, CGBO is tested with over 30 CEC2017 benchmark functions and a parameter optimization problem of the dendritic neuron model (DNM). Experimental results indicate that CGBO performs better than other state-of-the-art algorithms in terms of effectiveness and robustness.

scholarly journals Mycobacterium leprae diversity and population dynamics in medieval Europe from novel ancient genomes

BMC Biology ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Saskia Pfrengle ◽  
Judith Neukamm ◽  
Meriam Guellil ◽  
Marcel Keller ◽  
Martyna Molak ◽  
...  
Keyword(s):  
Mycobacterium Leprae ◽  
Population Diversity ◽  
Global History ◽  
Early Medieval ◽  
Long Distance ◽  
Early Medieval Period ◽  
The Past ◽  
New Genotype ◽  
Medieval Europe ◽  
High Diversity

Abstract Background Hansen’s disease (leprosy), widespread in medieval Europe, is today mainly prevalent in tropical and subtropical regions with around 200,000 new cases reported annually. Despite its long history and appearance in historical records, its origins and past dissemination patterns are still widely unknown. Applying ancient DNA approaches to its major causative agent, Mycobacterium leprae, can significantly improve our understanding of the disease’s complex history. Previous studies have identified a high genetic continuity of the pathogen over the last 1500 years and the existence of at least four M. leprae lineages in some parts of Europe since the Early Medieval period. Results Here, we reconstructed 19 ancient M. leprae genomes to further investigate M. leprae’s genetic variation in Europe, with a dedicated focus on bacterial genomes from previously unstudied regions (Belarus, Iberia, Russia, Scotland), from multiple sites in a single region (Cambridgeshire, England), and from two Iberian leprosaria. Overall, our data confirm the existence of similar phylogeographic patterns across Europe, including high diversity in leprosaria. Further, we identified a new genotype in Belarus. By doubling the number of complete ancient M. leprae genomes, our results improve our knowledge of the past phylogeography of M. leprae and reveal a particularly high M. leprae diversity in European medieval leprosaria. Conclusions Our findings allow us to detect similar patterns of strain diversity across Europe with branch 3 as the most common branch and the leprosaria as centers for high diversity. The higher resolution of our phylogeny tree also refined our understanding of the interspecies transfer between red squirrels and humans pointing to a late antique/early medieval transmission. Furthermore, with our new estimates on the past population diversity of M. leprae, we gained first insights into the disease’s global history in relation to major historic events such as the Roman expansion or the beginning of the regular transatlantic long distance trade. In summary, our findings highlight how studying ancient M. leprae genomes worldwide improves our understanding of leprosy’s global history and can contribute to current models of M. leprae’s worldwide dissemination, including interspecies transmissions.

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