Bees algorithm for multimodal function optimisation

The aim of multimodal optimisation is to find significant optima of a multimodal objective function including its global optimum. Many real-world applications are multimodal optimisation problems requiring multiple optimal solutions. The Bees Algorithm is a global optimisation procedure inspired by the foraging behaviour of honeybees. In this paper, several procedures are introduced to enhance the algorithm’s capability to find multiple optima in multimodal optimisation problems. In the proposed Bees Algorithm for multimodal optimisation, dynamic colony size is permitted to automatically adapt the search effort to different objective functions. A local search approach called balanced search technique is also proposed to speed up the algorithm. In addition, two procedures of radius estimation and optima elitism are added, to respectively enhance the Bees Algorithm’s ability to locate unevenly distributed optima, and eliminate insignificant local optima. The performance of the modified Bees Algorithm is evaluated on well-known benchmark problems, and the results are compared with those obtained by several other state-of-the-art algorithms. The results indicate that the proposed algorithm inherits excellent properties from the standard Bees Algorithm, obtaining notable efficiency for solving multimodal optimisation problems due to the introduced modifications. </jats:p

Direct And Evolutionary Approaches For Optimal Receiver Function Inversion

Receiver functions are time series obtained by deconvolving vertical component seismograms from radial component seismograms. Receiver functions represent the impulse response of the earth structure beneath a seismic station. Generally, receiver functions consist of a number of seismic phases related to discontinuities in the crust and upper mantle. The relative arrival times of these phases are correlated with the locations of discontinuities as well as the media of seismic wave propagation. The Moho (Mohorovicic discontinuity) is a major interface or discontinuity that separates the crust and the mantle. In this research, automatic techniques to determine the depth of the Moho from the earth’s surface (the crustal thickness H) and the ratio of crustal seismic P-wave velocity (Vp) to S-wave velocity (Vs) (ï«= Vp/Vs) were developed. In this dissertation, an optimization problem of inverting receiver functions has been developed to determine crustal parameters and the three associated weights using evolutionary and direct optimization techniques

Fitness Proportionate Niching: Harnessing The Power Of Evolutionary Algorithms For Evolving Cooperative Populations And Dynamic Clustering

Evolutionary algorithms work on the notion of best fit will survive criteria. This makes evolving a cooperative and diverse population in a competing environment via evolutionary algorithms a challenging task. Analogies to species interactions in natural ecological systems have been used to develop methods for maintaining diversity in a population. One such area that mimics species interactions in natural systems is the use of niching. Niching methods extend the application of EAs to areas that seeks to embrace multiple solutions to a given problem. The conventional fitness sharing technique has limitations when the multimodal fitness landscape has unequal peaks. Higher peaks are strong population attractors. And this technique suffers from the curse of population size in attempting to discover all optimum points. The use of high population size makes the technique computationally complex, especially when there is a big jump in fitness values of the peaks. This work introduces a novel bio-inspired niching technique, termed Fitness Proportionate Niching (FPN), based on the analogy of finite resource model where individuals share the resource of a niche in proportion to their actual fitness. FPN makes the search algorithm unbiased to the variation in fitness values of the peaks and hence mitigates the drawbacks of conventional fitness sharing. FPN extends the global search ability of Genetic Algorithms (GAs) for evolving hierarchical cooperation in genetics-based machine learning and dynamic clustering. To this end, this work introduces FPN based resource sharing which leads to the formation of a viable default hierarchy in classifiers for the first time. It results in the co-evolution of default and exception rules, which lead to a robust and concise model description. The work also explores the feasibility and success of FPN for dynamic clustering. Unlike most other clustering techniques, FPN based clustering does not require any a priori information on the distribution of the data