Fish Inspired Algorithms

Xing, Bo; Gao, Wenjing

doi:10.1007/978-3-319-03404-1_9

Cited by 3 publications

(1 citation statement)

References 78 publications

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“…In the last two decades, researchers have proposed other algorithms inspired from the social and biological behaviors of species. In the literature we talk about swarm based intelligence [6][7][8]. The swarm bioinspired algorithms tend to imitate a given social behavior of some insects, weed, and birds.…”

Section: Introductionmentioning

confidence: 99%

Rate learning-based fish school search algorithm for global optimization

Aguercif

Tighzert

Mendil

et al. 2017

2017 6th International Conference on Systems and Control (ICSC)

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In this paper, we propose a new variant of fish school search algorithm, called rate-learning based fish school search algorithm (RL-FSSA), that uses a new refinement strategy to guide the population of fishes towards the best solutions. The fish motion is based on a collective sensorimotor behavior. Their learning ability determines the fish with the best position (i.e., leader). In each cycle, the best fish ever found is refined by a rate learning based process. This refinement allows a local search and decreases the effect of the hazard through time, and the whole algorithm goes from diversification towards intensification. In order to evaluate its performance, the proposed algorithm is tested under a set of benchmark functions. The numerical tests include unimodal and multimodal functions. The results show the high performance of RL-FSSA compared to the standard version FSSA. Furthermore, the computational time of the fish school seach algorithm is reduced. For the validation, the algorithm is used for the trajectory planning and control of the mobile robot in an environment containing obstacles.I.

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Section: Introductionmentioning

confidence: 99%

Rate learning-based fish school search algorithm for global optimization

Aguercif

Tighzert

Mendil

et al. 2017

2017 6th International Conference on Systems and Control (ICSC)

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Energy Aware Optimized Hierarchical Routing Technique for Wireless Sensor Networks

Hamza

El-Said

Attia

et al. 2018

Advances in Intelligent Systems and Computing

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A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

477

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Fish Inspired Algorithms

Abstract: In this chapter, we present several fish algorithms that are inspired by

Cited by 3 publications

References 78 publications

Rate learning-based fish school search algorithm for global optimization

Rate learning-based fish school search algorithm for global optimization

Energy Aware Optimized Hierarchical Routing Technique for Wireless Sensor Networks

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

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