A novel comparative study between dual population genetic algorithm and artificial bee colony algorithm for function optimization

Alam, Faria; Saadi, Hossain Shaikh; Alam, Mohammad Shafiul

doi:10.1109/iccitechn.2016.7860219

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Fei et al [23] selected nine search positions to initialize the Afs for motion estimation. Zhu et al [24] and Gao et al [25] used the chaotic transformation [26] method to generate a more stable and uniform population. Kang et al [27] used a uniform initialization method to initialize the population, while Liu et al [28] initialized the Afs based on the optimization problem in hand.…”

Section: Fish Swarm Algorithm (Fsa)mentioning

confidence: 99%

“…In Eqs. ( 23) and (24), t is the current iteration, A and C are coefficients vectors, X* is the position vector of the best solution. The vector A and C are calculated using Eqs.…”

Section: Conceptmentioning

confidence: 99%

“…EHO is meta-heuristic simulated behaviour in herds of elephants [24] introduced by Wang. Optimize solution for global optimization tasks [5].…”

Section: Conceptmentioning

confidence: 99%

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Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

Devika¹,

Karegowda²

2023

Artificial Intelligence

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In the last few years, bio-inspired optimization techniques have been widely adopted in fields such as computer science, mathematics, and biology in order to optimize solutions. Bio inspired optimization problems are usually nonlinear and restricted to multiple nonlinear constraints to tackle the problems of the traditional optimization algorithms, the recent trends tend to apply bio-inspired optimization algorithms which represent a promising approach for solving complex optimization problems. This work comprises state-of-art of ten recent bio-inspired algorithms, gap analysis, and its applications namely; Particle swarm optimization (PSO), Genetic Bee Colony (GBC) Algorithm, Fish Swarm Algorithm (FSA), Cat Swarm Optimization (CSO), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Elephant Search Algorithm (ESA), Cuckoo Search Optimization Algorithm (CSOA), Moth flame optimization (MFO), and Grey Wolf Optimization (GWO) algorithm. The previous related works collected from Scopus databases are presented. Also, we explore some key issues in optimization and some applications for further research. We also analyze in-depth discussions on the essence of these algorithms and their connections to self-organization and their applications in different areas of research are presented. As a result, the proposed analysis of these algorithms leads to some key problems that have to be addressed in the future.

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Section: Fish Swarm Algorithm (Fsa)mentioning

confidence: 99%

“…In Eqs. ( 23) and (24), t is the current iteration, A and C are coefficients vectors, X* is the position vector of the best solution. The vector A and C are calculated using Eqs.…”

Section: Conceptmentioning

confidence: 99%

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Bio-inspired Optimization: Algorithm, Analysis and Scope of Application

Devika¹,

Karegowda²

2023

Artificial Intelligence

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“…Finally, the roulette selection strategy adopted by the algorithm is highly random, which makes the convergence speed of the algorithm slow. The dual population [21], [22] is an effective method to enhance the diversity of the population. Through the sharing of information among the populations, the co-evolution of the population can be realized.…”

Section: Introductionmentioning

confidence: 99%

A Fireworks Algorithm for the System-Level Fault Diagnosis Based on MM* Model

Gui

2019

IEEE Access

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Aiming at the characteristics of MM* model fault diagnosis, a fireworks algorithm based on a dual population strategy is designed. The dual population of the algorithm is operated independently in parallel, and cooperative operator and optimal operator are cross-executed in the iterative process. The cooperative operator enables two populations to exchange effective information, avoiding the premature maturity of the algorithm. The optimal operator helps to strengthen the global search power of the algorithm and improve the convergence rate of the algorithm. At the same time, the constraint equation is designed, a new fitness function is proposed, and the mutation operator and selection strategy are optimized. The experimental comparison shows that the algorithm improves the efficiency and accuracy of system-level fault diagnosis and has good practicability. Finally, the correctness of the algorithm is proved by theory, and the time complexity of the algorithm is analyzed. INDEX TERMS System-level fault diagnosis, MM* model, dual population, fireworks algorithm, t-diagnosable systems.

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