Collaborating Authors

Empowering swarm-based optimizers by multi-scale search to enhance Gradient Descent initialization performance Machine Learning

Swarm-based optimizers like Particle Swarm Optimization or Imperialistic Competitive Algorithm that act under influences of cooperation or competition among groups, are unable to search in multiple volumes of locality or globality and do not have nested localities. As hybrid optimizers, they may not give satisfactory results as initializers in Gradient Descent approximators used in plenty of multimodal problems like nonlinear subspace learning and neural network training, which have hierarchies of convex spaces due to nonlinearity and multi-layer nature of these models. To search in various levels of scale in a homogenous way, a framework is proposed to equip PSO and ICA a multi-scale search capability. Then, the resulted optimizers are evaluated in single and GD-hybridized mode. Hybrid evaluation as GD randomizer is implemented with the help of a nonlinear subspace filtering objective function over EEG data and optimization loss and validation data accuracy is compared with other hybrids containing GD. A single evaluation is also taken place between the proposed ones, PSO, ICA, CLPSO, and CICA, which are used more in hybrid learning-based approaches. Evaluations were with respect to solution error. Before concluding the paper, it is shown and analyzed that proposed optimizers outperform algorithms of related context both in single and hybrid-GD mode.

Heuristic Optimization of Electrical Energy Systems: A Perpetual Motion Scheme and Refined Metrics to Compare the Solutions Artificial Intelligence

Many optimization problems admit a number of local optima, among which there is the global optimum. For these problems, various heuristic optimization methods have been proposed. Comparing the results of these solvers requires the definition of suitable metrics. In the electrical energy systems literature, simple metrics such as best value obtained, the mean value, the median or the standard deviation of the solutions are still used. However, the comparisons carried out with these metrics are rather weak, and on these bases a somehow uncontrolled proliferation of heuristic solvers is taking place. This paper addresses the overall issue of understanding the reasons of this proliferation, showing that the assessment of the best solver can be cast into a perpetual motion scheme. Moreover, this paper shows how the use of more refined metrics defined to compare the optimization result, associated with the definition of appropriate benchmarks, may make the comparisons among the solvers more robust. The proposed metrics are based on the concept of first-order stochastic dominance and are defined for the cases in which: : (i) the globally optimal solution can be found (for testing purposes); and (ii) the number of possible solutions is so large that practically it cannot be guaranteed that the global optimum has been found. Illustrative examples are provided for a typical problem in the electrical energy systems area - distribution network reconfiguration. The conceptual results obtained are generally valid to compare the results of other optimization problems.

Evolutionary Computation, Optimization and Learning Algorithms for Data Science Machine Learning

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms.

Binary Sine Cosine Algorithms for Feature Selection from Medical Data Machine Learning

A well-constructed classification model highly depends on input feature subsets from a dataset, which may contain redundant, irrelevant, or noisy features. This challenge can be worse while dealing with medical datasets. The main aim of feature selection as a pre-processing task is to eliminate these features and select the most effective ones. In the literature, metaheuristic algorithms show a successful performance to find optimal feature subsets. In this paper, two binary metaheuristic algorithms named S-shaped binary Sine Cosine Algorithm (SBSCA) and V-shaped binary Sine Cosine Algorithm (VBSCA) are proposed for feature selection from the medical data. In these algorithms, the search space remains continuous, while a binary position vector is generated by two transfer functions S-shaped and V-shaped for each solution. The proposed algorithms are compared with four latest binary optimization algorithms over five medical datasets from the UCI repository. The experimental results confirm that using both bSCA variants enhance the accuracy of classification on these medical datasets compared to four other algorithms.

Social Algorithms Artificial Intelligence

This article concerns the review of a special class of swarm intelligence based algorithms for solving optimization problems and these algorithms can be referred to as social algorithms. Social algorithms use multiple agents and the social interactions to design rules for algorithms so as to mimic certain successful characteristics of the social/biological systems such as ants, bees, bats, birds and animals.