An improved adaptive memetic differential evolution optimization algorithms for data clustering problems

Enhanced Connectivity Validity Measure Based on Outlier Detection for Multi-Objective Metaheuristic Data Clustering Algorithms

Data clustering algorithms experience challenges in identifying data points that are either noise or outlier. Hence, this paper proposes an enhanced connectivity measure based on the outlier detection approach for multi-objective data clustering problems. The proposed algorithm aims to improve the quality of the solution by utilising the local outlier factor method (LOF) with the connectivity validity measure. This modification is applied to select the neighbour data point’s mechanism that can be modified to eliminate such outliers. The performance of the proposed approach is assessed by applying the multi-objective algorithms to eight real-life and seven synthetic two-dimensional datasets. The external validity is evaluated using the F-measure, while the performance assessment matrices are employed to assess the quality of Pareto-optimal sets like the coverage and overall non-dominant vector generation. Our experimental results proved that the proposed outlier detection method has enhanced the performance of the multi-objective data clustering algorithms.

A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines

The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results

Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al₂O₃/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd's variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.

Location problem of Osmia cornuta nesting aids for optimum pollination

The economic contribution of insect pollinators is evident as they contribute to higher crop yield quantity and quality. The management of bee species is key to crop production, especially where wild and domesticated bees are in low abundance. Several bee species have been identified as possible candidates for replacing, or at least supplementing, the decreasing number of honey bees. Our research seeks to address the location problem as regards nesting aids for Osmia cornuta bees in orchards using mathematical programming models for determining the optimal location of nesting aids and optimizing the management of solitary bees. A differential evolution algorithm is used to solve a location model of Osmia cornuta nesting aids for optimum pollination. Instead of a random ad hoc location of nesting aids in an orchard, or at the edge of an orchard, we utilize an effective optimization tool to determine locations which will optimize pollination by alternative pollinators, and increase the economic output of an agricultural business. The importance of this proposed model is likely to increase with agricultural intensification, and the decrease of the numbers of wild pollinators.

Solving text clustering problem using a memetic differential evolution algorithm

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.