A heuristic swarm-based optimization method using multi-variate normal distributions with self-adaptive variance matrices

Structures ◽  
2022 ◽  
Vol 36 ◽  
pp. 372-391
Author(s):  
Iman Shojaei ◽  
Hossein Rahami
Keyword(s):  
Optimization Method ◽  
Normal Distributions ◽  
Self Adaptive

Differential Evolution with Self-Adaptation

2011 ◽  
pp. 488-493 ◽  
Author(s):  
Janez Brest
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Objective Function ◽  
Optimization Method ◽  
Control Parameters ◽  
Research Areas ◽  
De Algorithm ◽  
Practical Engineering ◽  
Self Adaptation ◽  
Self Adaptive

Many practical engineering applications can be formulated as a global optimization problem, in which objective function has many local minima, and derivatives of the objective function are unavailable. Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces (Storn & Price, 1997) (Liu & Lampinen, 2005) (Price, Storn & Lampinen, 2005) (Feoktistov, 2006). Nowadays it is used as a powerful global optimization method within a wide range of research areas. Recent researches indicate that self-adaptive DE algorithms are considerably better than the original DE algorithm. The necessity of changing control parameters during the optimization process is also confirmed based on the experiments in (Brest, Greiner, Boškovic, Mernik, Žumer, 2006a). DE with self-adaptive control parameters has already been presented in (Brest et al., 2006a). This chapter presents self-adaptive approaches that were recently proposed for control parameters in DE algorithm.

scholarly journals Multiobjective Game Method Based on Self-Adaptive Space Division of Design Variables and Its Application to Vehicle Suspension

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Rui Meng ◽  
Nenggang Xie ◽  
Lu Wang
Keyword(s):  
Multiobjective Optimization ◽  
Iteration Process ◽  
Optimization Method ◽  
Payoff Function ◽  
Impact Factors ◽  
Vehicle Suspension ◽  
Space Division ◽  
Design Variables ◽  
Game Players ◽  
Self Adaptive

Based on the similarity between the game theory and the multiobjective design, the bionic mapping and the space mapping are established between the multiobjective optimization model and game model. Then, the multiobjective optimization method based on self-adaptive space division of design variables is proposed. The design variables are divided into multiple strategy subspaces and are assigned to corresponding game players by calculating impact factors,K-means clustering, and correlation analysis. Strategy subspaces of game players are dynamically adjusted in the iteration process. In their own strategy subspaces, each game player takes their payoff function (the mapping of objective function) as monoobjective optimization. It gives the best strategy upon other players. And the best strategies of all players are combined into the group strategy in this game round. Triobjective optimization is carried out for vehicle suspension in this method and it is compared with the traditional game method. The results show that this method has better calculating automaticity and can effectively promote generalization of multiobjective game method and improve the computational efficiency and precision.

scholarly journals PEMBOBOTAN KATA BERDASARKAN KLASTER PADA OPTIMISASI COVERAGE, DIVERSITY DAN COHERENCE UNTUK PERINGKASAN MULTI DOKUMEN

2016 ◽  
Vol 2 (3) ◽  
Author(s):  
Ryfial Azhar ◽  
Muhammad Machmud ◽  
Hanif Affandi Hartanto ◽  
Agus Zainal Arifin ◽  
Diana Purwitasari
Keyword(s):  
Differential Evolution ◽  
Text Analysis ◽  
Additional Term ◽  
Optimization Method ◽  
Term Weighting ◽  
High Coverage ◽  
Document Summarization ◽  
Adaptive Differential Evolution ◽  
Coherence Level ◽  
Self Adaptive

[Id]Peringkasan yang baik dapat diperoleh dengan coverage, diversity dan coherence yang optimal. Namun, terkadang sub-sub topik yang terkandug dalam dokumen tidak terekstrak dengan baik, sehingga keterwakilan setiap sub-sub topik tersebut tidak ada dalam hasil peringkasan dokumen. Pada paper ini diusulkan metode baru pembobotan kata berdasarkan klaster pada optimisasi coverage, diversity dan coherence untuk peringkasan multi-dokumen. Metode optimasi yang digunakan ialah self-adaptive differential evolution (SaDE) dengan penambahan pembobotan kata berdasarkan hasil dari pembentukan cluster dengan metode Similarity Based Histogram Clustering (SHC). Metode SHC digunakan untuk mengklaster kalimat sehingga setiap sub-topik pada dokumen bisa terwakili dalam hasil peringkasan. Metode SaDE digunakan untuk mencari solusi hasil ringkasan yang memiliki tingkat coverage, diversity, dan coherence paling tinggi. Uji coba dilakukan pada 15 topik dataset Text Analysis Conference (TAC) 2008. Hasil uji coba menunjukkan bahwa metode yang diusulkan dapat menghasilkan ringkasan skor ROUGE-1 sebesar 0.6704, ROUGE-2 sebesar 0.2051, ROUGE-L sebesar 0.6271 dan ROUGE-SU sebesar 0.3951.Kata kunci : peringkasan multi dokumen, similarity based histogram clustering, coverage, diversity, coherence[En]Good summary can be obtained with optimizing coverage, diversity, and coherence. Nevertheless, sometime sub-topics wich is contained in the document is not extracted well, so that the representation of each sub-topic is appear in docment summarizarion result. In this paper, we propose new of term weighting based on? cluster in optimizing coverage, diversity, and coherence for multi-document summarization. Optimization method which is used is self-adaptive differential evolution (SaDE) with additional term weighting based on clustering result with Similarity Based Histogram Clustering (SHC). SHC is used to cluster sentence so that every sub-topic in the document can be represented in summarization result. SaDE is used to search summarization result solution which has high coverage, diversity, and coherence level. Experiment is done on 15 topics in Text Analysis Conference (TAC) 2008 dataset. Experimental results show that this proposed method can produce summarization score? ROUGE-1 0.6704, ROUGE-2 0.2051, ROUGE-L 0.6271 and ROUGE-SU 0.3951.Keywords: multy-document summarization, similarity based histogram clustering, coverage, diversity, coherence.

Self-Adaptive Compensation Method on the Tool Position of Roller Gear Cam Surface

2010 ◽  
Vol 154-155 ◽  
pp. 396-400
Author(s):  
Rong Yu Ge ◽  
Qing Song Wang ◽  
Xu Qiang Shang
Keyword(s):  
Rotation Angle ◽  
Optimization Method ◽  
Compensation Method ◽  
Adaptive Compensation ◽  
Machining Error ◽  
Flexible Compensation ◽  
Tool Position ◽  
Tool Diameter ◽  
Adaptive Tool ◽  
Self Adaptive

The roller gear cam surface is often machined by the unequal diameter manufacture method, which means the tool diameter is smaller than that of the roller and the tool position compensation method is most used. For tool position of roller gear cam, it is important to confirm the compensation vector for the tool position compensation method, including compensation value and direction. In the paper, a new self-adaptive tool position optimization method is proposed, which make minimizing the normal machining error as the object function and make two compensation factors as the optimization variables. This algorithm can find out the best compensation direction and value by the tool position optimization for any cam rotation angle and make the tool position self-adaptive and flexible compensation according to the machining error. A numerical calculation example shows that the optimization algorithm can feasibly reduce the machining error. At last a conclusion has been drawn that the radius difference between the tool and the roller is the best compensation value and the best compensation direction is not fixed.

scholarly journals Self-Adaptive Artificial Bee Colony for Function Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Mingzhu Tang ◽  
Wen Long ◽  
Huawei Wu ◽  
Kang Zhang ◽  
Yuri A. W. Shardt
Keyword(s):  
Local Search ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Population Based ◽  
Function Optimization ◽  
Initial Population ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Global And Local ◽  
Self Adaptive

Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.

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