scholarly journals Biogeography-Based Optimization with Orthogonal Crossover

2013 ◽  
Vol 2013 ◽  
pp. 1-20 ◽  
Author(s):  
Quanxi Feng ◽  
Sanyang Liu ◽  
Guoqiang Tang ◽  
Longquan Yong ◽  
Jianke Zhang
Keyword(s):  
Orthogonal Design ◽  
Optimal Solution ◽  
Solution Space ◽  
Population Based ◽  
Population Diversity ◽  
Crossover Operator ◽  
Migration Operator ◽  
Migration Equation ◽  
Population Based Algorithm ◽  
Share Information

Biogeography-based optimization (BBO) is a new biogeography inspired, population-based algorithm, which mainly uses migration operator to share information among solutions. Similar to crossover operator in genetic algorithm, migration operator is a probabilistic operator and only generates the vertex of a hyperrectangle defined by the emigration and immigration vectors. Therefore, the exploration ability of BBO may be limited. Orthogonal crossover operator with quantization technique (QOX) is based on orthogonal design and can generate representative solution in solution space. In this paper, a BBO variant is presented through embedding the QOX operator in BBO algorithm. Additionally, a modified migration equation is used to improve the population diversity. Several experiments are conducted on 23 benchmark functions. Experimental results show that the proposed algorithm is capable of locating the optimal or closed-to-optimal solution. Comparisons with other variants of BBO algorithms and state-of-the-art orthogonal-based evolutionary algorithms demonstrate that our proposed algorithm possesses faster global convergence rate, high-precision solution, and stronger robustness. Finally, the analysis result of the performance of QOX indicates that QOX plays a key role in the proposed algorithm.

scholarly journals HOPX Crossover Operator for the Fixed Charge Logistic Model with Priority Based Encoding

2018 ◽  
Vol 8 (6) ◽  
pp. 5351
Author(s):  
Ahmed Lahjouji El Idrissi ◽  
Chakir Tajani ◽  
Mohamed Sabbane
Keyword(s):  
Optimal Solution ◽  
Solution Space ◽  
Fixed Charge ◽  
Fixed Cost ◽  
Crossover Operator ◽  
Fixed Charge Transportation Problem ◽  
Logistic Problem ◽  
Probability Operators ◽  
Different Characteristics

In this paper, we are interested to an important Logistic problem modelised us optimization problem. It is the fixed charge transportation problem (FCTP) where the aim is to find the optimal solution which minimizes the objective function containig two costs, variable costs proportional to the amount shipped and fixed cost regardless of the quantity transported. To solve this kind of problem, metaheuristics and evolutionary methods should be applied. Genetic algorithms (GAs) seem to be one of such hopeful approaches which is based both on probability operators (Crossover and mutation) responsible for widen the solution space. The different characteristics of those operators influence on the performance and the quality of the genetic algorithm. In order to improve the performance of the GA to solve the FCTP, we propose a new adapted crossover operator called HOPX with the priority-based encoding by hybridizing the characteristics of the two most performent operators, the Order Crossover (OX) and Position-based crossover (PX). Numerical results are presented and discussed for several instances showing the performance of the developed approach to obtain optimal solution in reduced time in comparison to GAs with other crossover operators.

scholarly journals Differential Evolution Dynamics Modeled by Longitudinal Social Network

2017 ◽  
Vol 26 (3) ◽  
pp. 523-529 ◽  
Author(s):  
Lenka Skanderova ◽  
Tomas Fabian ◽  
Ivan Zelinka
Keyword(s):  
Social Network ◽  
Differential Evolution ◽  
Short Interval ◽  
Optimal Solution ◽  
Population Based ◽  
De Algorithm ◽  
Parent Selection ◽  
Evolution Dynamics ◽  
Population Improvement ◽  
Population Based Algorithm

AbstractDifferential evolution (DE) is a population-based algorithm using Darwinian and Mendel principles to find out an optimal solution to difficult problems. In this work, the dynamics of the DE algorithm are modeled by using a longitudinal social network. Because a population of the DE algorithm is improved in generations, each generation of DE algorithm is represented by one short-interval network. Each short-interval network is created by individuals contributing to population improvement. On the basis of this model, a new parent selection in the mutation operation is presented and a well-known benchmark set CEC 2013 Special Session on Real-Parameter Optimization (including 28 functions) is used to evaluate the performance of the proposed algorithm.

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 Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Juan-Ignacio Latorre-Biel ◽  
Emilio Jiménez-Macías ◽  
Mercedes Pérez de la Parte ◽  
Julio Blanco-Fernández ◽  
Eduardo Martínez-Cámara
Keyword(s):  
Discrete Event ◽  
Optimal Solution ◽  
Solution Space ◽  
Discrete Systems ◽  
Discrete Control ◽  
Classic Approach ◽  
Event Dynamic ◽  
Time Required ◽  
Computational Resources ◽  
Control Methodology

Artificial intelligence methodologies, as the core of discrete control and decision support systems, have been extensively applied in the industrial production sector. The resulting tools produce excellent results in certain cases; however, the NP-hard nature of many discrete control or decision making problems in the manufacturing area may require unaffordable computational resources, constrained by the limited available time required to obtain a solution. With the purpose of improving the efficiency of a control methodology for discrete systems, based on a simulation-based optimization and the Petri net (PN) model of the real discrete event dynamic system (DEDS), this paper presents a strategy, where a transformation applied to the model allows removing the redundant information to obtain a smaller model containing the same useful information. As a result, faster discrete optimizations can be implemented. This methodology is based on the use of a formalism belonging to the paradigm of the PN for describing DEDS, the disjunctive colored PN. Furthermore, the metaheuristic of genetic algorithms is applied to the search of the best solutions in the solution space. As an illustration of the methodology proposal, its performance is compared with the classic approach on a case study, obtaining faster the optimal solution.

Predictive Search for Capacitated Multi-Item Lot Sizing Problems

2021 ◽  
Author(s):  
Tao Wu
Keyword(s):  
Mathematical Programming ◽  
Heuristic Search ◽  
Lot Sizing ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Method ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Programming Technique ◽  
Advanced Analytics

For capacitated multi-item lot sizing problems, we propose a predictive search method that integrates machine learning/advanced analytics, mathematical programming, and heuristic search into a single framework. Advanced analytics can predict the probability that an event will happen and has been applied to pressing industry issues, such as credit scoring, risk management, and default management. Although little research has applied such technique for lot sizing problems, we observe that advanced analytics can uncover optimal patterns of setup variables given properties associated with the problems, such as problem attributes, and solution values yielded by linear programming relaxation, column generation, and Lagrangian relaxation. We, therefore, build advanced analytics models that yield information about how likely a solution pattern is the same as the optimum, which is insightful information used to partition the solution space into incumbent, superincumbent, and nonincumbent regions where an analytics-driven heuristic search procedure is applied to build restricted subproblems. These subproblems are solved by a combined mathematical programming technique to improve solution quality iteratively. We prove that the predictive search method can converge to the global optimal solution point. The discussion is followed by computational tests, where comparisons with other methods indicate that our approach can obtain better results for the benchmark problems than other state-of-the-art methods. Summary of Contribution: In this study, we propose a predictive search method that integrates machine learning/advanced analytics, mathematical programming, and heuristic search into a single framework for capacitated multi-item lot sizing problems. The advanced analytics models are used to yield information about how likely a solution pattern is the same as the optimum, which is insightful information used to divide the solution space into incumbent, superincumbent, and nonincumbent regions where an analytics-driven heuristic search procedure is applied to build restricted subproblems. These subproblems are solved by a combined mathematical programming technique to improve solution quality iteratively. We prove that the predictive search method can converge to the global optimal solution point. Through computational tests based on benchmark problems, we observe that the proposed approach can obtain better results than other state-of-the-art methods.

scholarly journals A Novel Method for Economic Dispatch with Across Neighborhood Search: A Case Study in a Provincial Power Grid, China

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Guojiang Xiong ◽  
Jing Zhang ◽  
Xufeng Yuan ◽  
Dongyuan Shi ◽  
Yu He ◽  
...  
Keyword(s):  
Power Grid ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Search Space ◽  
Neighborhood Search ◽  
Solution Quality ◽  
Promising Alternative ◽  
Comparison Results ◽  
Population Based Algorithm

Economic dispatch (ED) is of cardinal significance for the power system operation. It is mathematically a typical complex nonlinear multivariable strongly coupled optimization problem with equality and inequality constraints, especially considering the valve-point effects. In order to effectively solve the problem, a simple yet very young and efficient population-based algorithm named across neighborhood search (ANS) is implemented in this paper. In ANS, a group of individuals collaboratively navigate through the search space for obtaining the optimal solution by simultaneously searching the neighborhoods of multiple superior solutions. Four benchmark test cases with diverse complexities and characteristics are firstly employed to comprehensively verify the feasibility and effectiveness of ANS. The experimental and comparison results fully demonstrate the superiority of ANS in terms of the final solution quality, convergence speed, robustness, and statistics. In addition, the sensitivities of ANS to variations of population size and across-search degree are studied. Furthermore, ANS is applied to a practical provincial power grid of China. All the comparison results consistently indicate that ANS is highly competitive and can be used as a promising alternative for ED problems.

scholarly journals Aspects of the methodology of plant populations research (by means of the example of rare species)

2020 ◽  
pp. 115-128
Author(s):  
Volodymyr Kyyak ◽  
Andriy Malynovskyі
Keyword(s):  
Research Methodology ◽  
Rare Species ◽  
Vegetative Reproduction ◽  
Integral Characteristic ◽  
Life Forms ◽  
Population Based ◽  
Population Diversity ◽  
Isolated Populations ◽  
Long Time ◽  
Dynamic Endurance

In this work basic concepts and problematic issues of modern population ecology of plants are considered. Quite different views on many aspects of terminology and research methodology one can observed in the scientific literature. Basic terms such as: «vitality», «age structure», «ontogenetic structure», «self-renewal», «strategy», «regeneration niche», «buffering», population size establishment, etc can be interpreted differently, which causes significant discrepancies in research methodology, and thus are reflected in the interpretation of results. The population is considered as an indivisible and unique set of individuals of one species, which is characterized by self-reproduction while maintaining its main properties. The statemen concerning the population as a unit of biodiversity accountability is justified. The study of strategy, intra-population diversity, viability and mechanisms of self-recovery has shown that the existence of populations for a long time is ensured by a certain structure. The structure of the population is a set of quantitative and qualitative components of its organization at the individual and group level, which characterize the structure and ensure the functioning of the population as a whole system. At the same time, establishing which structural elements play a leading role in the changed environmental conditions continues to be an urgent task for species of different life forms in the variety of conditions of their habitats. The example of isolated populations of rare species shows the most typical stages of structure change due to pessimization of living conditions: simplification of spatial structure, aging or pseudo-rejuvenation, reduction of vitality of individuals and population loci, reduction of self-sufficiency and reproduction, simplification and reduction of ontogenesis of individuals, predominance of death rate over birth rate, reduction of area, number and density, cessation of formation of generative individuals, cessation of undergrowth replenishment, reduction of vegetative reproduction, loss of population viability. An integral characteristic that reflects the current state of the population based on the most important individual and group parameters of structure, growth, development and reproduction is the vitality of the population. Intrapopulation structural components - population loci of reproduction - are of the greatest importance in preserving the viability of populations and ensuring the prospects of their existence in the event of adverse changes in the environment. Among population structures, the highest dynamic endurance and stability of functioning for a long time under different conditions of natural and anthropogenically altered environment were found in metapopulations. Understanding of the processes taking place on the verge of existence of the population during its emergence, and especially during the period of its loss of viability and extinction, is extremely important. However, there are still not so many researches aimed at revealing the patterns of behavior of populations during these most vulnerable periods of their long life cycle.

Hybrid Data Mining Approach for Image Segmentation Based Classification

Biometrics ◽  
2017 ◽  
pp. 1543-1561 ◽  
Author(s):  
Mrutyunjaya Panda ◽  
Aboul Ella Hassanien ◽  
Ajith Abraham
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Image Segmentation ◽  
Search Algorithm ◽  
Rbf Neural Network ◽  
Harmony Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Harmony Search Algorithm ◽  
Data Mining Technique

Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

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