/pdf/the-dance-language-and-orientation-of-bees-3jdlph0pp0.pdf

The Dance Language and Orientation of Bees

Particle swarm optimization (PSO). A tutorial

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Nonlinear Functional Analysis And Its Applications

African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.

Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems

Load flow problem has a great significance to analyze the power system network due to its roll in planning and operation of network. Generally, Newton–Raphson (NR) method is used to analyze the load flow problems due to its efficiency and accuracy. But, NR method has some inherent drawbacks like in-efficient for highly loaded network, assumption are required for initial values and abnormal operating conditions. To overcome the existing drawbacks of NR method, a recently developed swarm intelligence based algorithm, namely Gbest guided Artificial Bee Colony algorithm (GABC) is applied to solve the load flow problem for five bus network. The reported results of GABC are compared to the results of NR method and basic ABC algorithm, which show that the accuracy of unknown parameters such as voltage, angle and power produced by GABC is competitive method to the NR method and basic ABC algorithm based method.

Gbest-Artificial Bee Colony Algorithm to Solve Load Flow Problem

The optimal control problem consists of a performance index subject to a set of differential equations that describes the path of the control and state variables. The main aim of this article is to prove the existence and uniqueness of a mild solution, optimal control, and time‐optimal control of a mixed Volterra–Fredholm‐type third‐order dispersion system. By applying the strongly continuous semigroup theory and the Banach fixed‐point theorem, we prove the existence and uniqueness of the considered system. The optimal control results are proved by using Mazur's lemma, Gronwall's inequality, and the minimizing sequence technique. The discussion on the time‐optimal control of the third‐order dispersion system is also presented.

A note on the existence and optimal control for mixed Volterra–Fredholm‐type integrodifferential dispersion system of third order

Contemporary software systems are used in various walks of life, in diverse environments that differs from the environment in which it was tested. The software reliability is evaluated based on the past failure data with no regard to software environment in the development process. In this paper, an improved Non-homogeneous Poisson Process based software reliability model is proposed by including vagueness in operating environment. The uncertainty due to operating environment is incorporated in the form of a generalized two-parameter probability density function. The proposed model is validated using secondary data set which performs considerably better than the present models.

An improved srgm considering uncertain operating environment

Artificial bee colony ABC optimisation algorithm is relatively a recent, fast and easy to implement population-based meta heuristic for optimisation. ABC has been proved a competitive algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. However, it is observed that ABC algorithm is better at exploration but poor at exploitation. Due to large step size, the solution search equation of ABC has enough chance to skip the optimum. In order to balance this, ABC is hybridised with a local search called as classical unidimensional search CUS. The proposed algorithm is named as hybridised ABC HABC. In HABC, best solution of each iteration is further exploited in both its positive and negative direction in a predefined range which enhances the exploitation in ABC. The experiments are carried out on 15 test problems of different complexities and dimensions in order to prove the efficiency of proposed algorithm and compared with ABC. The results shows that hybridisation of CUS with ABC improves the performance of ABC.

Hybridisation of classical unidimensional search with ABC to improve exploitation capability

This article deals with the optimal control of the fractional stochastic
 nonlocal semilinear system in Hilbert space. The existence and uniqueness
 results for the mild solution are derived using Banach fixed point theorem.
 The optimal control is proved using minimizing sequence approach and Mazur?s
 lemma. For better understanding of theory, we have included one example.

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Shimpi Singh Jadon

Papers

Gbest-Artificial Bee Colony Algorithm to Solve Load Flow Problem

A note on the existence and optimal control for mixed Volterra–Fredholm‐type integrodifferential dispersion system of third order

An improved srgm considering uncertain operating environment

Hybridisation of classical unidimensional search with ABC to improve exploitation capability

Optimal control problem for fractional stochastic nonlocal semilinear system