Improved dynamical particle swarm optimization method for structural dynamics

A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version

Evolution of Swarm Robotics Systems with Novelty Search

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task - aggregation, and a more challenging task - sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping the evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.Comment: To appear in Swarm Intelligence (2013), ANTS Special Issue. The final publication will be available at link.springer.co

HEURISTICS OPTIMISATION OF NUMERICAL FUNCTIONS

The article presents an investigation of heuristic behaviour of search algorithms applied to numerical problems. The aim is to compare the abilities of Particle Swarm Optimisation, Differential Evolution and Free Search to adapt to variety of search spaces without the need for constant re-tuning of algorithms parameters. The article focuses on several advanced characteristics of Free Search and attempts to clarify specifics of its behaviour. The achieved experimental results are presented and discussed

Free Search of real value or how to make computers think

This book introduces in detail Free Search - a novel advanced method for search and optimisation. It also deals with some essential questions that have been raised in a strong debate following the publication of this method in journal and conference papers. In the light of this debate, Free Search deserves serious attention, as it appears to be superior to other competitive methods in the context of the experimental results obtained. This superiority is not only quantitative in terms of the actual optimal value found but also qualitative in terms of independence from initial conditions and adaptation capabilities in an unknown environment

Firefly Algorithm: Recent Advances and Applications

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.Comment: 15 page

Adaptive intelligence: essential aspects

The article discusses essential aspects of Adaptive Intelligence. Experimental results on optimisation of global test functions by Free Search, Differential Evolution, and Particle Swarm Optimisation clarify how these methods can adapt to multi-modal landscape and space dominated by sub-optimal regions, without supervisors’ control. The achieved results are compared and analysed

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

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

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners