Compound particle swarm optimization in dynamic environments

Copyright @ Springer-Verlag Berlin Heidelberg 2008.Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO) is proposed as a new variant of particle swarm optimization to enhance its performance in dynamic environments. Within CPSO, compound particles are constructed as a novel type of particles in the search space and their motions are integrated into the swarm. A special reflection scheme is introduced in order to explore the search space more comprehensively. Furthermore, some information preserving and anti-convergence strategies are also developed to improve the performance of CPSO in a new environment. An experimental study shows the efficiency of CPSO in dynamic environments.This work was supported by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant No. 70431003 and Grant No. 70671020, the Science Fund for Creative Research Group of NNSF of China under Grant No. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant No. EP/E060722/1

Particle swarm optimization with composite particles in dynamic environments

This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Feedback learning particle swarm optimization

This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published and is available at the link below - Copyright @ Elsevier 2011In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSO-QIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by the generation number but also by the search environment described by each particle’s history best fitness information. Thirdly, the feedback fitness information of each particle is used to automatically design the learning probabilities. Fourthly, an elite stochastic learning (ELS) method is used to refine the solution. The FLPSO-QIW has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art PSO algorithms, the performance of FLPSO-QIW is promising and competitive. The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany

Kajian terhadap ketahanan hentaman ke atas konkrit berbusa yang diperkuat dengan serat kelapa sawit

Konkrit berbusa merupakan sejenis konkrit ringan yang mempunyai kebolehkerjaan yang baik dan tidak memerlukan pengetaran untuk proses pemadatan. Umum mengenali konkrit berbusa sebagai bahan binaan yang mempunyai sifat kekuatan yang rendah dan lemah terutama apabila bahan binaan ini dikenakan tenaga hentaman yang tinggi. Namun begitu, konkrit berbusa merupakan bahan yang berpotensi untuk dijadikan sebagai bahan binaan yang berkonsepkan futuristik. Binaan futuristik adalah binaan yang bercirikan ringan, ekonomi, mudah dari segi kerja pembinaan dan yang paling penting adalah mesra alam. Dalam kajian ini, konkrit berbusa ditambah serat buangan pokok kelapa sawit untuk untuk meningkatkan sifat kekuatan atau rapuh. Serat kelapa sawit juga berfungsi mempertingkatkan ketahanan hentaman terutamanya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman. Kandungan peratusan serat kelapa sawit yang digunakan adalah 10%, 20% dan 30% dengan dua ketumpatan konkrit berbusa iaitu 1000kg/m3 dan 1400kg/m3 . Untuk menentukan nilai penyerapan tenaga hentaman dan nilai tenaga hentaman, ujikaji Indentasi dan ujikaji hentaman dilakukan ke atas sampel�sampel yang telah diawet selama 28 hari. Luas bawah graf tegasan-terikan yang diperolehi daripada ujikaji Indentasi merupakan nilai penyerapan tenaga hentaman bagi sampel konkrit berbusa. Untuk ujikaji hentaman, keputusan ujikaji dinilai berdasarkan nilai tenaga hentaman untuk meretakkan sampel yang diperolehi daripada mesin ujikaji dynatup. Secara keseluruhannya, hasil dapatan utama bagi kedua-dua ujikaji menunjukkan sampel yang mengandungi peratusan serat kelapa sawit sebanyak 20% mempunyai nilai penyerapan tenaga hentaman dan nilai tenaga hentaman yang tinggi. Serapan tenaga maksimum adalah sebanyak 4.517MJ/m3 untuk ketumpatan 1400kg/m3 . Ini menunjukkan ketumpatan 1400kg/m3 berupaya menyerap tenaga lebih baik berbanding ketumpatan 1000kg/m3 . Manakala untuk nilai tenaga hentaman maksimum adalah sebanyak 27.229J untuk ketumpatan 1400kg/m3 . Hasil dapatan tersebut menunjukkan ketumpatan 1400kg/m3 dengan peratusan serat sebanyak 20% berupaya mengalas tenaga hentaman yang lebih banyak sebelum sampel retak. Kesimpulannya, peningkatan ketumpatan konkrit berbusa dan pertambahan serat buangan kelapa sawit ke dalam konkrit berbusa dapat meningkatkan ciri ketahanan hentaman konkrit berbusa khususnya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman

Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms

The problem of parameterization is often central to the effective deployment of nature-inspired algorithms. However, finding the optimal set of parameter values for a combination of problem instance and solution method is highly challenging, and few concrete guidelines exist on how and when such tuning may be performed. Previous work tends to either focus on a specific algorithm or use benchmark problems, and both of these restrictions limit the applicability of any findings. Here, we examine a number of different algorithms, and study them in a "problem agnostic" fashion (i.e., one that is not tied to specific instances) by considering their performance on fitness landscapes with varying characteristics. Using this approach, we make a number of observations on which algorithms may (or may not) benefit from tuning, and in which specific circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial Life (ECAL) 2013, Taormina, Ital