A Novel Optimization Method for Ontology Matching Based on Heuristic Population Evolution Algorithm

2018 ◽  
Vol 44 (4) ◽  
pp. 3137-3153
Author(s):  
Feng Zhang ◽  
Zongliang Guo
Keyword(s):  
Optimization Method ◽  
Ontology Matching ◽  
Population Evolution ◽  
Evolution Algorithm

scholarly journals Simplified Phasmatodea population evolution algorithm for optimization

2021 ◽  
Author(s):  
Pei-Cheng Song ◽  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan ◽  
Hongmei Yang
Keyword(s):  
Population Growth ◽  
Path Dependence ◽  
Optimization Problems ◽  
Stick Insect ◽  
Insect Population ◽  
Engineering Optimization ◽  
Population Evolution ◽  
Evolution Path ◽  
Evolution Characteristics ◽  
Evolution Algorithm

AbstractThis work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.

The Phasmatodea Population Evolution Algorithm and Its Application in 5G Heterogeneous Network Downlink Power Allocation Problem

2021 ◽  
Vol 22 (6) ◽  
pp. 1199-1213
Author(s):  
Jeng-Shyang Pan Jeng-Shyang Pan ◽  
Pei-Cheng Song Jeng-Shyang Pan ◽  
Chun-An Pan Pei-Cheng Song ◽  
Ajith Abraham Chun-An Pan
Keyword(s):  
Power Allocation ◽  
Heterogeneous Network ◽  
Allocation Problem ◽  
Population Evolution ◽  
Evolution Algorithm

scholarly journals Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-36 ◽  
Author(s):  
Wei Li ◽  
Lei Wang ◽  
Quanzhu Yao ◽  
Qiaoyong Jiang ◽  
Lei Yu ◽  
...  
Keyword(s):  
Solid State ◽  
Differential Evolution ◽  
Liquid State ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Gaseous State ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Cloud Particles

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

Multi-Agent Order Selection and its Optimization Method with Evolution Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1066-1071
Author(s):  
Li Zhang ◽  
Jie Wu Zhang ◽  
Wei Jun Ma
Keyword(s):  
Optimization Algorithm ◽  
Model Simulation ◽  
Optimization Method ◽  
Business Environment ◽  
Autonomous Agent ◽  
Fast Time ◽  
Architecture Model ◽  
Rapid Changes ◽  
Multi Agent ◽  
Evolution Algorithm

The rapid changes of market and outer surroundings have caused a dynamic and highly volatile business environment for the enterprise. Manufacturing organizations are seeking efficiency gains by competing via fast time-to-market and low production cost. An Autonomous Agent Network(ANN) Based Manufacturing System model is introduced, as well as the mathematics expression for the components of AAN model. The optimization algorithm based on evolution algorithm is used to solve the optimization problem of the model. Simulation results show that the architecture model and its optimization algorithm are effective to the problem.

Optimization of FLADE Variable Cycle Engine Performance Based on Improved Differential Evolution Algorithm

2017 ◽  
Author(s):  
Zhang Xiao-bo ◽  
Wang Zhan-xue
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Engine Performance ◽  
Optimization Method ◽  
Performance Simulation ◽  
De Algorithm ◽  
Civil Aircraft ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Integrated Performance

In this paper, a double bypass variable cycle engine with FLADE (Fan on Blade) is considered. The FLADE VCE is one of the research hotspots for future military and civil aircraft power device, which shows outstanding performance advantages. Compared to the mixed-flow turbofan, FLADE VCE is more complex than conventional aero-engine for its multi-components and multi-variable parts, which make it difficult to modeling and optimization. For getting the performance of FLADE VCE, the model for engine performance simulation is researched. The method for FLADE performance simulation and the steady-state performance simulation model for FLADE VCE are developed. And a component-based engine performance simulation system is established based on object-oriented modeling method. For obtaining the optimal integrated performance of FLADE VCE, suitable optimization method is required. Unfortunately, the optimization of FLADE VCE is a non-linear non-differentiable problem, which makes it difficult to solve by conventional deterministic optimization method. In order to solve this problem, the differential evolution (DE) algorithm is considered. To overcome the limitations of original DE algorithm, an improved DE algorithm with modifying mutation operator is proposed by this paper. The FLADE VCE optimization problem is solved by employing the improved DE algorithm.

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