A MOS-based Dynamic Memetic Differential Evolution Algorithm for Continuous Optimization: A Scalability Test

Continuous optimization is one of the areas with more activity in the field of heuristic optimization. Many algorithms have been proposed and compared on several benchmarks of functions, with different performance depending on the problems. For this reason, the combination of different search strategies seems desirable to obtain the best performance of each of these approaches. This contribution explores the use of a hybrid memetic algorithm based on the multiple offspring framework. The proposed algorithm combines the explorative/exploitative strength of two heuristic search methods that separately obtain very competitive results. This algorithm has been tested with the benchmark problems and conditions defined for the special issue of the Soft Computing Journal on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. The proposed algorithm obtained the best results compared with both its composing algorithms and a set of reference algorithms that were proposed for the special issue

A Differential Evolution Framework with Ensemble of Parameters and Strategies and Pool of Local Search Algorithms

The file attached to this record is the author's final peer reviewed version. The publisher's final version can be found by following the DOI link.The ensemble structure is a computational intelligence supervised strategy consisting of a pool of multiple operators that compete among each other for being selected, and an adaptation mechanism that tends to reward the most successful operators. In this paper we extend the idea of the ensemble to multiple local search logics. In a memetic fashion, the search structure of an ensemble framework cooperatively/competitively optimizes the problem jointly with a pool of diverse local search algorithms. In this way, the algorithm progressively adapts to a given problem and selects those search logics that appear to be the most appropriate to quickly detect high quality solutions. The resulting algorithm, namely Ensemble of Parameters and Strategies Differential Evolution empowered by Local Search (EPSDE-LS), is evaluated on multiple testbeds and dimensionality values. Numerical results show that the proposed EPSDE-LS robustly displays a very good performance in comparison with some of the state-of-the-art algorithms

‘Viral’ hunts? A cultural Darwinian analysis of witch persecutions

The theory of Darwinian cultural evolution is gaining currency in many parts of the socio-cultural sciences, but it remains contentious. Critics claim that the theory is either fundamentally mistaken or boils down to a fancy re-description of things we knew all along. We will argue that cultural Darwinism can indeed resolve long-standing socio-cultural puzzles; this is demonstrated through a cultural Darwinian analysis of the European witch persecutions. Two central and unresolved questions concerning witch-hunts will be addressed. From the fifteenth to the seventeenth centuries, a remarkable and highly specific concept of witchcraft was taking shape in Europe. The first question is: who constructed it? With hindsight, we can see that the concept contains many elements that appear to be intelligently designed to ensure the continuation of witch persecutions, such as the witches’ sabbat, the diabolical pact, nightly flight, and torture as a means of interrogation. The second question is: why did beliefs in witchcraft and witch-hunts persist and disseminate, despite the fact that, as many historians have concluded, no one appears to have substantially benefited from them? Historians have convincingly argued that witch-hunts were not inspired by some hidden agenda; persecutors genuinely believed in the threat of witchcraft to their communities. We propose that the apparent ‘design’ exhibited by concepts of witchcraft resulted from a Darwinian process of evolution, in which cultural variants that accidentally enhanced the reproduction of the witch-hunts were selected and accumulated. We argue that witch persecutions form a prime example of a ‘viral’ socio-cultural phenomenon that reproduces ‘selfishly’, even harming the interests of its human hosts

A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems

Copyright @ 2011 Taylor & Francis.Many real-world optimisation problems are both dynamic and multi-modal, which require an optimisation algorithm not only to find as many optima under a specific environment as possible, but also to track their moving trajectory over dynamic environments. To address this requirement, this article investigates a memetic computing approach based on particle swarm optimisation for dynamic multi-modal optimisation problems (DMMOPs). Within the framework of the proposed algorithm, a new speciation method is employed to locate and track multiple peaks and an adaptive local search method is also hybridised to accelerate the exploitation of species generated by the speciation method. In addition, a memory-based re-initialisation scheme is introduced into the proposed algorithm in order to further enhance its performance in dynamic multi-modal environments. Based on the moving peaks benchmark problems, experiments are carried out to investigate the performance of the proposed algorithm in comparison with several state-of-the-art algorithms taken from the literature. The experimental results show the efficiency of the proposed algorithm for DMMOPs.This work was supported by the Key Program of National Natural Science Foundation (NNSF) of China under Grant no. 70931001, the Funds for Creative Research Groups of China under Grant no. 71021061, the National Natural Science Foundation (NNSF) of China under Grant 71001018, Grant no. 61004121 and Grant no. 70801012 and the Fundamental Research Funds for the Central Universities Grant no. N090404020, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant no. EP/E060722/01 and Grant EP/E060722/02, and the Hong Kong Polytechnic University under Grant G-YH60

Automatically Modeling Hybrid Evolutionary Algorithms from Past Executions

ection of the most appropriate Evolutionary Algorithm for a given optimization problem is a difficult task. Hybrid Evolutionary Algorithms are a promising alternative to deal with this problem. By means of the combination of different heuristic optimization approaches, it is possible to profit from the benefits of the best approach, avoiding the limitations of the others. Nowadays, there is an active research in the design of dynamic or adaptive hybrid algorithms. However, little research has been done in the automatic learning of the best hybridization strategy. This paper proposes a mechanism to learn a strategy based on the analysis of the results from past executions. The proposed algorithm has been evaluated on a well-known benchmark on continuous optimization. The obtained results suggest that the proposed approach is able to learn very promising hybridization strategies

Novel Memetic Computing Structures for Continuous Optimisation

This thesis studies a class of optimisation algorithms, namely Memetic Computing Structures, and proposes a novel set of promising algorithms that move the first step towards an implementation for the automatic generation of optimisation algorithms for continuous domains. This thesis after a thorough review of local search algorithms and popular meta-heuristics, focuses on Memetic Computing in terms of algorithm structures and design philosophy. In particular, most of the design carried out during my doctoral studies is inspired by the lex parsimoniae, aka Ockham’s Razor. It has been shown how simple algorithms, when well implemented can outperform complex implementations. In order to achieve this aim, the design is always carried out by attempting to identify the role of each algorithmic component/operator. In this thesis, on the basis of this logic, a set of variants of a recently proposed algorithms are presented. Subsequently a novel memetic structure, namely Parallel Memetic Structure is proposed and tested against modern algorithms representing the state of the art in optimisation. Furthermore, an initial prototype of an automatic design platform is also included. This prototype performs an analysis on separability of the optimisation problem and, on the basis of the analysis results, designs some parts of the parallel structure. Promising results are included. Finally, an investigation of the correlation among the variables and problem dimensionality has been performed. An extremely interesting finding of this thesis work is that the degree of correlation among the variables decreases when the dimensionality increases. As a direct consequence of this fact, large scale problems are to some extent easier to handle than problems in low dimensionality since, due to the lack of correlation among the variables, they can effectively be tackled by an algorithm that performs moves along the axes

Technological Innovations and Advances in Hydropower Engineering

It has been more than 140 years since water was used to generate electricity. Especially since the 1970s, with the advancement of science and technology, new technologies, new processes, and new materials have been widely used in hydropower construction. Engineering equipment and technology, as well as cascade development, have become increasingly mature, making possible the construction of many high dams and large reservoirs in the world. However, with the passage of time, hydropower infrastructure such as reservoirs, dams, and power stations built in large numbers in the past are aging. This, coupled with singular use of hydropower, limits the development of hydropower in the future. This book reports the achievements in hydropower construction and the efforts of sustainable hydropower development made by various countries around the globe. These existing innovative studies and applications stimulate new ideas for the renewal of hydropower infrastructure and the further improvement of hydropower development and utilization efficiency