Chaotic Firefly Algorithm for Solving Definite Integral

Abdel-Raouf, Osama; Abdel-Baset, Mohamed; El-Henawy, Ibrahim M.

doi:10.5815/ijitcs.2014.06.03

Cited by 12 publications

(10 citation statements)

References 35 publications

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“…Many metaheuristic search algorithms have been employed for FS to search for (near) optimal subset of features from these large volume datasets, as they prove their superiority in bringing out a better performance. Some of the most popular metaheuristic algorithms are Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO), algorithms inspired by fish schools [3], Gravity search [4], different aspects of the behaviour of bees [5], Fireflies [6], Bats [7], Cuckoo birds [8], etc. Newly proposed modifications in search heuristics like chaotic maps [9], Sine Cosine Algorithm [10], Evolutionary methods [11], Local searches [12], and Biogeography Based Optimization [13] have also improved the performance of the search heuristics internally.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid SCA Inspired BBO for Feature Selection Problems

Sindhu

Ngadiran

Yacob

et al. 2019

Mathematical Problems in Engineering

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Recent trend of research is to hybridize two and more metaheuristics algorithms to obtain superior solution in the field of optimization problems. This paper proposes a newly developed wrapper-based feature selection method based on the hybridization of Biogeography Based Optimization (BBO) and Sine Cosine Algorithm (SCA) for handling feature selection problems. The position update mechanism of SCA algorithm is introduced into the BBO algorithm to enhance the diversity among the habitats. In BBO, the mutation operator is got rid of and instead of it, a position update mechanism of SCA algorithm is applied after the migration operator, to enhance the global search ability of Basic BBO. This mechanism tends to produce the highly fit solutions in the upcoming iterations, which results in the improved diversity of habitats. The performance of this Improved BBO (IBBO) algorithm is investigated using fourteen benchmark datasets. Experimental results of IBBO are compared with eight other search algorithms. The results show that IBBO is able to outperform the other algorithms in majority of the datasets. Furthermore, the strength of IBBO is proved through various numerical experiments like statistical analysis, convergence curves, ranking methods, and test functions. The results of the simulation have revealed that IBBO has produced very competitive and promising results, compared to the other search algorithms.

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Section: Introductionmentioning

confidence: 99%

A Hybrid SCA Inspired BBO for Feature Selection Problems

Sindhu

Ngadiran

Yacob

et al. 2019

Mathematical Problems in Engineering

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“…Moreover, it depends on its initial condition and parameters [36][37][38]. Applications of chaos has several disciplines including operations research, physics, engineering, economics, biology, philosophy and computer science [39][40][41].…”

Section: Chaos Theorymentioning

confidence: 99%

An Improved Chaotic Bat Algorithm for Solving Integer Programming Problems

Raouf¹,

Baset²,

Henawy³

2014

IJMECS

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Abstract-BatAlgorithm is a recently-developed method in the field of computational intelligence. In this paper is presented an improved version of a Bat Meta-heuristic Algorithm, (IBACH), for solving integer programming problems. The proposed algorithm uses chaotic behaviour to generate a candidate solution in behaviors similar to acoustic monophony. Numerical results show that the IBACH is able to obtain the optimal results in comparison to traditional methods (branch and bound), particle swarm optimization algorithm (PSO), standard Bat algorithm and other harmony search algorithms. However, the benefits of this proposed algorithm is in its ability to obtain the optimal solution within less computation, which save time in comparison with the branch and bound algorithm (exact solution method).

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“…Applications of chaos has several disciplines including operations research, physics, engineering, economics, biology, philosophy and computer science [35][36][37]. Recently chaos has been extended to various optimization areas because it can more easily escape from local minima and improve global convergence in comparison with other stochastic optimization algorithms [34][35][36][37][38]. Using chaotic sequences in flower pollination Algorithm can be helpful to improve the reliability of the global optimality, and they also enhance the quality of the results.…”

Section: Chaosmentioning

confidence: 99%

“…At random-based optimization algorithms, the methods using chaotic variables instead of random variables are called chaotic optimization algorithms (COA) [34]. In these algorithms, due to the non-repetition and ergodicity of chaos, it can carry out overall searches at higher speeds than stochastic searches that depend on probabilities [43][44][45][46][47][48].…”

Section: Chaotic Mapsmentioning

confidence: 99%

“…Chaos is a deterministic, random-like process found in a nonlinear, dynamical system, which is non-period, non-converging and non-bounded. Moreover, it depends on its initial condition and parameters [32][33][34]. Applications of chaos has several disciplines including operations research, physics, engineering, economics, biology, philosophy and computer science [35][36][37].…”

Section: Chaosmentioning

confidence: 99%

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A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming Problems

Ismail¹,

El-Henawy²

2015

IJCATR

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Branch and Bound technique (B&B) is commonly used for intelligent search in finding a set of integer solutions within a space of interest. The corresponding binary tree structure provides a natural parallelism allowing concurrent evaluation of subproblems using parallel computing technology. Flower pollination Algorithm is a recently-developed method in the field of computational intelligence. In this paper is presented an improved version of Flower pollination Meta-heuristic Algorithm, (FPPSO), for solving integer programming problems. The proposed algorithm combines the standard flower pollination algorithm (FP) with the particle swarm optimization (PSO) algorithm to improve the searching accuracy. Numerical results show that the FPPSO is able to obtain the optimal results in comparison to traditional methods (branch and bound) and other harmony search algorithms. However, the benefits of this proposed algorithm is in its ability to obtain the optimal solution within less computation, which save time in comparison with the branch and bound algorithm.

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Chaotic Firefly Algorithm for Solving Definite Integral

Cited by 12 publications

References 35 publications

A Hybrid SCA Inspired BBO for Feature Selection Problems

A Hybrid SCA Inspired BBO for Feature Selection Problems

An Improved Chaotic Bat Algorithm for Solving Integer Programming Problems

A Comparison between FPPSO and B&B Algorithm for Solving Integer Programming Problems

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