A new swarm intelligence information technique for improving information balancedness on the skin lesions segmentation

Methods of image processing can recognize the images of melanoma lesions border in addition to the disease compared to a skilled dermatologist. New swarm intelligence technique depends on meta-heuristic that is industrialized to resolve composite real problems which are problematic to explain by the available deterministic approaches. For an accurate detection of all segmentation and classification of skin lesions, some dealings should be measured which contain, contrast broadening, irregularity quantity, choice of most optimal features, and so into the world. The price essential for the action of progressive disease cases is identical high and the survival percentage is low. Many electronic dermoscopy classifications are advanced depend on the grouping of form, surface and dye features to facilitate premature analysis of malignance. To overcome this problematic, an effective prototypical for accurate boundary detection and arrangement is obtainable. The projected classical recovers the optimization segment of accuracy in its pre-processing stage, applying contrast improvement of lesion area compared to the contextual. In conclusion, optimized features are future fed into of artifical bee colony (ABC) segmentation. Wide-ranging researches have been supported out on four databases named as, ISBI (2016, 2017, 2018) and PH2. Also, the selection technique outclasses and successfully indifferent the dismissed features. The paper shows a different process for lesions optimal segmentation that could be functional to a variation of images with changed possessions and insufficiencies is planned with multistep pre-processing stage

A memetic algorithm based on Artificial Bee Colony for optimal synthesis of mechanisms

En este documento se presenta una propuesta novedosa de un algoritmo híbrido modular, como herramienta para resolver problemas de ingeniería del mundo real. Se implementa y aplica un algoritmo memético, MemMABC, para la solución de dos casos de diseño de mecanismos, con el fin de evaluar su eficiencia y rendimiento. El algoritmo propuesto es simple y flexible debido a su modularidad; estas características lo vuelven altamente reutilizable para ser aplicado en una amplia gama de problemas de optimización. Las soluciones de los casos de estudio también son modulares, siguiendo un esquema de programación estructurada que incluye el uso de variables globales para la configuración, y de subrutinas para la función objetivo y el manejo de las restricciones. Los algoritmos meméticos son una buena opción para resolver problemas duros de optimización, debido a la sinergia derivada de la combinación de sus componentes: una metaheurística poblacional para búsqueda global y un método de refinamiento local. La calidad en los resultados de las simulaciones sugiere que el MemMABC puede aplicarse con éxito para la solución de problemas duros de diseño en ingeniería.In this paper a novel proposal of a modular hybrid algorithm as a tool for solving real-world engineering problems is presented. A memetic algorithm, MemMABC, is implemented with this approach and applied to solve two case studies of mechanism design, in order to evaluate its efficiency and performance. Because of its modularity, the proposed algorithm is simple and flexible; these features make it quite reusable to be applied on different optimization problems, with a wide scope. The solutions of the optimization problems are also modular, following a scheme of structured programming that includes the use of global variables for configuration, and subroutines for the objective function and the restrictions. Memetic algorithms are a good option to solve hard optimization problems, because of the synergy derived from the combination of their components: a global search population-based metaheuristic and a local refinement method. The quality of simulation results suggests that MemMABC can be successfully applied to solve hard problems in engineering design.Peer Reviewe

應用蜂群演算法於結構最佳化設計之研究

[[abstract]]本論文應用蜂群演算法於結構最佳化設計中。蜂群演算法是一種模仿自然界蜜蜂覓食行為進行問題求解之方法，該法為具有群體智慧的仿生演算法，其特點為收斂速度快、參數設定少及搜尋範圍廣。蜂群演算法利用其獨特的雇用蜂以及非雇用蜂的方式進行大範圍的搜尋以尋求全域最佳解。過程中藉由食物源採用機率判斷是否採用當前最佳解，如此反覆搜尋直到找到全域最佳解為止。範例中將結構最佳化問題轉為數學函數，再利用蜂群演算法對結構系統執行最佳化設計。由數值分析範例之結果，發現應用蜂群演算法於結構最佳化設計上可得到不錯的結果。[[sponsorship]]淡江大學航空太空工程學系[[conferencetkucampus]]淡水校園[[conferencedate]]20141118~20141119[[booktype]]紙本[[conferencelocation]]淡水, 台