Genetic Particle Swarm Optimization Based on Estimation of Distribution

2007 ◽  
pp. 287-296 ◽  
Author(s):  
Jiahai Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Estimation Of Distribution

scholarly journals Penyelesaian Masalah Penempatan Fasilitas dengan Algoritma Estimasi Distribusi dan Particle Swarm Optimization

2016 ◽  
Vol 2 (1) ◽  
pp. 11
Author(s):  
Amalia Utamima ◽  
Angelia Melani Andrian
Keyword(s):  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Facility Layout ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Estimation Of Distribution ◽  
Np Complete ◽  
Estimation Distribution Algorithm

Abstrak—Masalah penempatan fasilitas pada garis lurus dikenal sebagai problem Penempatan Fasilitas pada Satu Baris (PFSB). Tujuan PFSB, yang dikategorikan sebagai masalah NP-Complete, adalah untuk mengatur tata letak sehingga jumlah jarak antara pasangan semua fasilitas bisa diminimalisir. Algoritma Estimasi Distribusi (EDA) meningkatkan kualitas solusi secara efisien dalam beberapa pengoperasian pertama, namun keragaman dalam solusi hilang secara pesat ketika semakin banyak iterasi dijalankan. Untuk menjaga keragaman, hibridisasi dengan algoritma meta-heuristik diperlukan. Penelitian ini mengusulkan EDAPSO, algoritma yang terdiri dari hibridisasi EDA dan Particle Swarm Optimization (PSO). Tujuan dari penelitian ini yaitu untuk menguji performa algoritma EDAPSO dalam menyelesaikan PFSB.Kinerja EDAPSO yang diuji dalam 10 masalah benchmark PFSB dan EDAPSO berhasil mencapai solusi optimal.Kata kunci—penempatan fasilitas, algoritma estimasi distribusi, particle swarm optimizationAbstract—The layout positioning problem of facilities on a straight line is known as Single Row Facility Layout Problem (PFSB). Categorized as NP-Complete problem, PFSB aim to arrange the layout so that the sum of distances between all facilities’ pairs can be minimized. Estimation of Distribution Algorithm (EDA) improves the solution quality efficiently in first few runs, but the diversity lost grows rapidly as more iterations are run. To maintain the diversity, hybridization with meta-heuristic algorithms is needed. This research proposes EDAPSO, an algorithm which consists of hybridization of EDA and Particle Swarm Optimization (PSO). The objective of this research is to test the performance of EDAPSO algorithm for solving PFSB.  EDAPSO’s performance is tested in 10 benchmark problems of PFSB and it successfully achieves optimum solution.Keywords— facility layout, estimation distribution algorithm, particle swarm optimization

scholarly journals An Integrated Method Based on PSO and EDA for the Max-Cut Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Geng Lin ◽  
Jian Guan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Estimation Of Distribution Algorithm ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Integrated Method ◽  
Benchmark Instances ◽  
Max Cut Problem ◽  
Local Search Procedure ◽  
Estimation Of Distribution

The max-cut problem is NP-hard combinatorial optimization problem with many real world applications. In this paper, we propose an integrated method based on particle swarm optimization and estimation of distribution algorithm (PSO-EDA) for solving the max-cut problem. The integrated algorithm overcomes the shortcomings of particle swarm optimization and estimation of distribution algorithm. To enhance the performance of the PSO-EDA, a fast local search procedure is applied. In addition, a path relinking procedure is developed to intensify the search. To evaluate the performance of PSO-EDA, extensive experiments were carried out on two sets of benchmark instances with 800 to 20000 vertices from the literature. Computational results and comparisons show that PSO-EDA significantly outperforms the existing PSO-based and EDA-based algorithms for the max-cut problem. Compared with other best performing algorithms, PSO-EDA is able to find very competitive results in terms of solution quality.

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