An Algorithm for Global Optimization Inspired by Collective Animal Behavior

Cuevas, Erik; González, Mauricio; Zaldívar, Daniel; Pérez‐Cisneros, Marco; García, Guillermo

doi:10.1155/2012/638275

Cited by 63 publications

(19 citation statements)

References 56 publications

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“…The results in Table 2 show that PSO, ABC, EM, CS, and FPA have similar values in their performance. The evidence shows that evolutionary algorithms maintain a similar average performance when they face unimodal lowdimensional functions [29,30]. In particular, the test remarks that the small difference in performance is directly related to a better exploitation mechanism included in CS and FPA.…”

Section: (1) a Plant With A Second-order System And A First-order Iirmentioning

confidence: 91%

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

Cuevas

Gálvez

Hinojosa

et al. 2014

Journal of Applied Mathematics

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System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.

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Section: (1) a Plant With A Second-order System And A First-order Iirmentioning

confidence: 91%

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

Cuevas

Gálvez

Hinojosa

et al. 2014

Journal of Applied Mathematics

Self Cite

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“…Following the approach of [5], [6], [28], CSB emulates the behavior of individuals based upon the relative position and orientation of individuals with respect to one another by applying local repulsion, alignment and attractive operators. In this approach, every individual position from the population represents a unique solution within the search space.…”

Section: Collective Social Behavior (Csb) Algorithmmentioning

confidence: 99%

“…The overall optimization process exhibits the collective social behavior in animal groups. In recent times, researchers have noticed that the previous history of the group social structure seems to influence collective behaviors as individual interactions change [5], [6], [28]. This indicates that the social behavior of animal groups exhibits a form of collective memory.…”

Section: Collective Social Behavior (Csb) Algorithmmentioning

confidence: 99%

Optimization of array pattern for efficient control of adaptive nulling and side lobe level

Sankar¹,

Kiong²,

Paw³

2015

2015 IEEE International Conference on Communication, Networks and Satellite (COMNESTAT)

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The array synthesis problem is modeled as a nonlinear optimization problem with the constraints of a reduced side lobe level (SLL). The goal is to impose deeper nulls in the interference direction of an uniform linear antenna arrays. This paper focuses on the array synthesis of adaptive nulling and side lobe level control which is a highly nonlinear problem fit for an evolutionary optimization algorithm. The limitations with the available optimization algorithms are mainly subjected to premature convergence and stagnation problem hence resulting in a non satisfactory array synthesis results. Hence, a new optimization technique known as Collective Social Behavior (CSB) is developed with concepts adopted from evolution of social behavior to address the current limitations to achieve a better result in array synthesis problem. The optimal set of weight coefficients for the linear antenna arrays is determined by the CSB algorithm by evaluating the objective function for the array synthesis problem. To establish the CSB algorithm as an efficient optimization tool for adaptive nulling applications, several numerical benchmark tests have been conducted. The simulation results reveal that the proposed CSB algorithm enhances the performance of array pattern synthesis with precise deeper nulls and suppressed side lobe levels.

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“…CAB is an optimization technique which mimics the collective behaviour of animals [12, 13]. CAB algorithm assumes the existence of a set of operations that resemble the interaction rules that model the collective animal behaviour.…”

Section: Optimization Technique Employedmentioning

confidence: 99%

“…The hyper beamforming/any other beamforming offers high detection performance like beam width, the target bearing estimation and reduces false alarm, sidelobe suppression. A new optimized hyper beamforming technique is presented in this paper, and collective animal behaviour (CAB) approach is applied to obtain optimal hyperbeam patterns [12, 13] of linear antenna arrays.…”

Section: Introductionmentioning

confidence: 99%

Optimized Hyper Beamforming of Linear Antenna Arrays Using Collective Animal Behaviour

Ram

Mandal

Kar

et al. 2013

The Scientific World Journal

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A novel optimization technique which is developed on mimicking the collective animal behaviour (CAB) is applied for the optimal design of hyper beamforming of linear antenna arrays. Hyper beamforming is based on sum and difference beam patterns of the array, each raised to the power of a hyperbeam exponent parameter. The optimized hyperbeam is achieved by optimization of current excitation weights and uniform interelement spacing. As compared to conventional hyper beamforming of linear antenna array, real coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE) applied to the hyper beam of the same array can achieve reduction in sidelobe level (SLL) and same or less first null beam width (FNBW), keeping the same value of hyperbeam exponent. Again, further reductions of sidelobe level (SLL) and first null beam width (FNBW) have been achieved by the proposed collective animal behaviour (CAB) algorithm. CAB finds near global optimal solution unlike RGA, PSO, and DE in the present problem. The above comparative optimization is illustrated through 10-, 14-, and 20-element linear antenna arrays to establish the optimization efficacy of CAB.

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An Algorithm for Global Optimization Inspired by Collective Animal Behavior

Cited by 63 publications

References 56 publications

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

Optimization of array pattern for efficient control of adaptive nulling and side lobe level

Optimized Hyper Beamforming of Linear Antenna Arrays Using Collective Animal Behaviour

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