Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Computational military tactical planning system

Abstract: A computational system called fuzzy-genetic decision optimization combines two soft computing methods, genetic optimization and fuzzy ordinal preference, and a traditional hard computing method, stochastic system simulation, to tackle the difficult task of generating battle plans for military tactical forces. Planning for a tactical military battle is a complex, high-dimensional task which often bedevils experienced professionals. In fuzzy-genetic decision optimization, the military commander enters his battle outcome preferences into a user interface to generate a fuzzy ordinal preference model that scores his preference for any battle outcome. A genetic algorithm iteratively generates populations of battle plans for evaluation in a stochastic combat simulation. The fuzzy preference model converts the simulation results into a fitness value for each population member, allowing the genetic algorithm to generate the next population. Evolution continues until the system produces a final population of high-performance plans which achieve the commander's intent for the mission. Analysis of experimental results shows that co-evolution of friendly and enemy plans by competing genetic algorithms improves the performance of the planning system. If allowed to evolve long enough, the plans produced by automated algorithms had a significantly higher mean performance than those generated by experienced military experts.

Soft computing based autonomous low rate ddosattack detection and security for cloud computing

Abstract: The fundamental advantage of the cloud environment is its instant scalability in rendering the service according to the various demands. The recent technological growth in the cloud computing makes it accessible to people from everywhere at any time. Multitudes of user utilizes the cloud platform for their various needs and store their complete details that are personnel as well as confidential in the cloud architecture. The storage of the confidential information makes the cloud architecture attractive to its hackers, who aim in misusing the confidential/secret information’s. The misuse of the services and the resources of the cloud architecture has become a common issue in the day to day usage due to the DDOS (distributed denial of service) attacks. The DDOS attacks are highly mature and continue to grow at a high speed making the detecting and the counter measures a challenging task. So the paper uses the soft computing based autonomous detection for the Low rate-DDOS attacks in the cloud architecture. The proposed method utilizes the hidden Markov Model for observing the flow in the network and the Random forest in classifying the detected attacks from the normal flow. The proffered method is evaluated to measure the performance improvement attained in terms of the Recall, Precision, specificity, accuracy and F-measure.

Energy saving in a wastewater treatment process: an application of fuzzy logic control.

Abstract: Many uncertain factors affect the operation of Wastewater Treatment Plants. Due to the complexity of biological wastewater treatment processes, classical methods show significant difficulties when trying to control them automatically. Consequently soft computing techniques and, specifically, fuzzy logic appears to be a good candidate for controlling these ill-defined, time-varying and non-linear systems. This paper describes the development and implementation of a Fuzzy Logic Controller to regulate the aeration in the Taradell Wastewater Treatment Plant. The main goal of this control process is to save energy without decreasing the quality of the effluent discharged. The fuzzy controller integrates the information coming from two different signals: the Dissolved Oxygen and Oxidation-Reduction Potential values. The simulation results proved that fuzzy logic is a good tool for controlling the aeration of the wastewater treatment plant. The results obtained show that energy savings of more than 10% can be ac...

Cuckoo Search Optimized Reduction and Fuzzy Logic Classifier for Heart Disease and Diabetes Prediction

Abstract: Disease forecasting using soft computing techniques is major area of research in data mining in recent years. To classify heart and diabetes diseases, this paper proposes a diagnosis system using cuckoo search optimized rough sets based attribute reduction and fuzzy logic system. The disease prediction is done as per the following steps 1 feature reduction using cuckoo search with rough set theory 2 Disease prediction using fuzzy logic system. The first step reduces the computational burden and enhances performance of fuzzy logic system. Second step is based on the fuzzy rules and membership functions which classifies the disease datasets. The authors have tested this approach on Cleveland, Hungarian, Switzerland heart disease data sets and a real-time diabetes dataset. The experimentation result demonstrates that the proposed algorithm outperforms the existing approaches.

Design of stochastic numerical solver for the solution of singular three-point second-order boundary value problems

Abstract: In this paper, a novel meta-heuristic computing solver is presented for solving the singular three-point second-order boundary value problems using artificial neural networks (ANNs) optimized by the combined strength of global and local search ability of genetic algorithms (GAs) and interior point algorithm (IPA), ie, ANN–GA–IPA The inspiration for presenting this numerical work comes from the intention of introducing a consistent framework that combines the effective features of neural networks optimized with the contexts of soft computing to handle with such challenging systems Three numerical variants of singular second-order system have been taken to examine the proficiency, robustness, and stability of the designed approach The comparison of the proposed results of ANN–GA–IPA from available exact solutions shows the good agreement with 5 to 7 decimal places of the accuracy which established worth of the methodology through performance analyses on a single and multiple executions