A global optimization algorithm inspired in the behavior of selfish herds

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

doi:10.1016/j.biosystems.2017.07.010

Cited by 141 publications

(46 citation statements)

References 22 publications

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“…The social behaviour of some animals preferably foraging in herds during an attack by predators to increase the survival chances is merely selfish with other conspecifics during aggregation, and this was mathematically formulated as SHO algorithm, proposed by Fausto et al 18 The detail theory, procedures, and codes for the algorithm are reported in Fausto et al 18 . The concept of selfish herd theory states that the animals within a herd try to shrink their predation threat by placing other conspecifics amongst themselves and predators as risk increases towards the periphery, but reduces towards the centre of aggregation.…”

Section: Quasi-oppositional Selfish Herd Optimisationmentioning

confidence: 99%

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

Barik

Das

2019

Int Trans Electr Energ Syst

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Summary This work is the earliest attempt towards coordinated regulation of both frequency and voltage of an isolated multisource‐based hybrid microgrid with demand response support using a modified selfish herd optimisation. It is a maiden effort to propose a 50‐Hz system frequency‐based hybrid microgrid model, utilising biorenewable sustainable energy sources like sunrays, wind, waste waters, and agricultural and domestic wastes, with available support from demand response contributors for a dual objective of waste and power management. The intermittency of these resources due to climatic variations creates a great challenge to meet the timely consumer demands of the microgrid with reliable quality power supply. To overcome these issues, the performances of the proposed system are studied considering different typical Indian‐climatic scenarios round the year with real‐time recorded data. Initially, the system performances are compared proposing a new objective function called integral square of weighted absolute error in order to minimise the net deviation in system apparent power, using six controllers tuned with four‐popular/recent optimisation algorithms and four‐objective functions in MATLAB. Then, the simulation results are analysed for five different extreme scenarios of both source and load variations in the proposed system using a novel quasi‐oppositional selfish herd optimisation and reported adaptive performances with demand response support. Finally, the sensitivity of the proposed system with variation in system inertia and load damping factor are analysed to study the robustness of the proposed system.

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Section: Quasi-oppositional Selfish Herd Optimisationmentioning

confidence: 99%

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

Barik

Das

2019

Int Trans Electr Energ Syst

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“…erefore, in current research, the HaeDE performance is compared to those of the aeDE [17], particle swarm optimization (PSO) [11], Selfish Herd Optimizer (SHO) [9], Salp Swarm Algorithm (SSA) [14], and Dragonfly algorithm (DA) [13] using 32/50 functions that are multimodal. For each benchmark function, all methods are run 30 independent times with the same initial population in each time.…”

Section: Experiments On Benchmark Functionsmentioning

confidence: 99%

“…Population-based algorithms including evolutionary algorithms and swarm-based algorithms are types of global searching techniques. Evolutionary algorithms [1][2][3][4][5][6][7][8] are inspired by biological processes that allow population to adapt to their surroundings: genetic inheritance and survival of the best chromosomes; swarm-based algorithms [9][10][11][12][13][14][15][16] that focus on the social behaviors of insects and animals can solve the optimal problem as well. Among popular evolutionary algorithms, the differential evolution (DE) algorithm firstly introduced by Storn and Price [8] has been used in many practical problems and has demonstrated good convergence properties.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Optimization Approach Using Adaptive Elitist Differential Evolution and Spherical Quadratic Steepest Descent and Its Application for Clustering

Nguyen‐Trang

Nguyen-Thoi

Truong-Khac

et al. 2019

Scientific Programming

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In this paper, a hybrid approach that combines a population-based method, adaptive elitist differential evolution (aeDE), with a powerful gradient-based method, spherical quadratic steepest descent (SQSD), is proposed and then applied for clustering analysis. This combination not only helps inherit the advantages of both the aeDE and SQSD but also helps reduce computational cost significantly. First, based on the aeDE’s global explorative manner in the initial steps, the proposed approach can quickly reach to a region that contains the global optimal value. Next, based on the SQSD’s locally effective exploitative manner in the later steps, the proposed approach can find the global optimal solution rapidly and accurately and hence helps reduce the computational cost. The proposed method is first tested over 32 benchmark functions to verify its robustness and effectiveness. Then, it is applied for clustering analysis which is one of the problems of interest in statistics, machine learning, and data mining. In this application, the proposed method is utilized to find the positions of the cluster centers, in which the internal validity measure is optimized. For both the benchmark functions and clustering problem, the numerical results show that the hybrid approach for aeDE (HaeDE) outperforms others in both accuracy and computational cost.

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“…This algorithm is used in some applications such as solving global optimization [13] and structural damage identification [14]. Selfish herd optimizer (SHO) [15] is another meta-heuristic that emulates selfish herd behavior. The SHO is used to solve global optimization problems [15], and there is another version of SHO that depends on the opposite-based learning method and that applied this algorithm to an optimization function [16].…”

Section: Introductionmentioning

confidence: 99%

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

Alresheedi

Elaziz

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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In data center companies, cloud computing can host multiple types of heterogeneous virtual machines (VMs) and provide many features, including flexibility, security, support, and even better maintenance than traditional centers. However, some issues need to be considered, such as the optimization of energy usage, utilization of resources, reduction of time consumption, and optimization of virtual machine placement. Therefore, this paper proposes an alternative multiobjective optimization (MOP) approach that combines the salp swarm and sine-cosine algorithms (MOS-SASCA) to determine a suitable solution for virtual machine placement (VMP). The objectives of the proposed MOSSASCA are to maximize mean time before a host shutdown (MTBHS), to reduce power consumption, and to minimize service level agreement violations (SLAVs). The proposed method improves the salp swarm and the sine-cosine algorithms using an MOP technique. The SCA works by using a local search approach to improve the performance of traditional SSA by avoiding trapping in a local optimal solution and by increasing convergence speed. To evaluate the quality of MOSSASCA, we perform a series of experiments using different numbers of VMs and physical machines. The results of MOSSASCA are compared with well-known methods, including the nondominated sorting genetic algorithm (NSGA-II), multiobjective particle swarm optimization (MOPSO), a multiobjective evolutionary algorithm with decomposition (MOEAD), and a multiobjective sine-cosine algorithm (MOSCA). The results reveal that MOSSASCA outperforms the compared methods in terms of solving MOP problems and achieving the three objectives. Compared with the other methods, MOSSASCA exhibits a better ability to reduce power consumption and SLAVs while increasing MTBHS. The main differences in terms of power consumption between the

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A global optimization algorithm inspired in the behavior of selfish herds

Cited by 141 publications

References 22 publications

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

Coordinated regulation of voltage and load frequency in demand response supported biorenewable cogeneration‐based isolated hybrid microgrid with quasi‐oppositional selfish herd optimisation

An Efficient Hybrid Optimization Approach Using Adaptive Elitist Differential Evolution and Spherical Quadratic Steepest Descent and Its Application for Clustering

Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing

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