Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

Słowik, Adam; Kwaśnicka, Halina

doi:10.1109/tii.2017.2786782

Cited by 198 publications

(110 citation statements)

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“…The combination of evolutionary algorithms (EAs) and swarm intelligence (SI) algorithms are commonly known as NIAs. The use of NIAs is popular and efficient in solving optimization problems in the research field [1]. EAs are inspired by Darwinian theory.…”

Section: Introductionmentioning

confidence: 99%

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Balande

Shrimankar

2019

Mathematics

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Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents an overview of the constraint handling techniques. It also includes a hybrid algorithm, namely the Stochastic Ranking with Improved Firefly Algorithm (SRIFA) for solving constrained real-world engineering optimization problems. The stochastic ranking approach is broadly used to maintain balance between penalty and fitness functions. FA is extensively used due to its faster convergence than other metaheuristic algorithms. The basic FA is modified by incorporating opposite-based learning and random-scale factor to improve the diversity and performance. Furthermore, SRIFA uses feasibility based rules to maintain balance between penalty and objective functions. SRIFA is experimented to optimize 24 CEC 2006 standard functions and five well-known engineering constrained-optimization design problems from the literature to evaluate and analyze the effectiveness of SRIFA. It can be seen that the overall computational results of SRIFA are better than those of the basic FA. Statistical outcomes of the SRIFA are significantly superior compared to the other evolutionary algorithms and engineering design problems in its performance, quality and efficiency.

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Section: Introductionmentioning

confidence: 99%

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Balande

Shrimankar

2019

Mathematics

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“…Another technique, PSO is quite suitable for various problems with many local minima, where gradient information is not readily available. It can be easily implemented and there are very few parameters to adjust [5]. Its convergence speed is faster and prediction of accuracy is also better than FFNN and GA.…”

Section: Introductionmentioning

confidence: 99%

Electric Forecasting using Nature Inspired Optimization Techniques

Singh*,

Srivastava,

Singh

2019

IJEAT

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Now a day, there exists huge competition among power industries in terms of fulfilling various customers' electrical needs. Reliable and quality power supply is no doubt a basic need for all power consumers. Moreover, planning & operation engineers also targets for proper unit commitment, economic power dispatch, etc., and highly depend upon good power system planning. Therefore, Electric Forecasting (EF) is a major criterion for power engineers. In this manuscript, Artificial Neural Network (ANN), being a well established tool for modeling non-linear and black box systems, is used to forecast hydro generation power plant, energy met and peak demand of India.Furthermore, in this competitive world, ANN model is further optimized using genetic algorithm (GA) and particle swarm optimization (PSO) to explore accurate forecasting model with minimal amount of error. These optimization methods explore highly diversified search area, resulting in more accurate forecasting results in comparison to ANN when trained with standard back propagation training algorithm. India. His research area includes power system planning, transformer, energy efficiency, load forecasting, neural network. Navneet Kumar Singh received the PhD degree from Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India. Currently he is with Electrical Engineering Department, MNNIT Allahabad, India. His research area includes load forecasting, power system planning and Application of Artificial Intelligence (AI) techniques in power system.

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“…This paper proposes a new method of selecting optimal parameters of the functions that vary the filter coefficients, where all necessary values are specified by the chosen algorithm, in this case the Particle Swarm Optimization (PSO) algorithm [5][6][7]. Using artificial intelligence methods is popular when it comes to filtration and methods are constantly being developed [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Concept of the Non-Stationary Filtering Network with Reduced Transient Response

Wiechetek

Piskorowski

2019

Applied Sciences

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This paper presents a concept of the non-stationary filtering network with reduced transient response consisting of the first-order digital elements with time-varying parameters. The digital filter section is based on the analog system. In order to design the filtering network, the analog prototype was subjected to the discretization process. The time constant and the gain factor were then temporarily varied in time in order to suppress the transient response of the designed filtering structure. The optimization method, based on the Particle Swarm Optimization (PSO) algorithm which is aimed at reducing the settling time by a proper parameter manipulation, is presented. Simulation results proving the usefulness of the proposed concept are also shown and discussed.

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Nature Inspired Methods and Their Industry Applications—Swarm Intelligence Algorithms

Cited by 198 publications

References 110 publications

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

Electric Forecasting using Nature Inspired Optimization Techniques

A Concept of the Non-Stationary Filtering Network with Reduced Transient Response

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