scholarly journals An Algorithm for Global Optimization Inspired by Collective Animal Behavior

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Erik Cuevas ◽  
Mauricio González ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Guillermo García
Keyword(s):  
Global Optimization ◽  
Animal Behavior ◽  
Food Source ◽  
Collective Motion ◽  
Global Optimum ◽  
Migration Routes ◽  
Collective Behaviors ◽  
Biological Laws ◽  
Harvesting Efficiency ◽  
Collective Animal Behavior

A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.

scholarly journals A Swarm Optimization Algorithm for Multimodal Functions and Its Application in Multicircle Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
Erik Cuevas ◽  
Daniel Zaldívar ◽  
Marco Pérez-Cisneros
Keyword(s):  
Animal Behavior ◽  
Optimization Algorithm ◽  
Engineering Problem ◽  
Migration Routes ◽  
Collective Behaviors ◽  
Cost Constraints ◽  
Engineering Problems ◽  
Biological Laws ◽  
Harvesting Efficiency ◽  
Evolutionary Operators

In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global) are known, the implementation can be quickly switched to another solution without much interrupting the design process. This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB). Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents emulate a group of animals which interact with each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi-circle detection, achieving satisfactory results.

scholarly journals Global optimization based on active preference learning with radial basis functions

2020 ◽  
Author(s):  
Alberto Bemporad ◽  
Dario Piga
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Decision Maker ◽  
Optimization Problems ◽  
Global Optimum ◽  
Bayesian Optimization ◽  
Preference Learning ◽  
Global Optimizer ◽  
Radial Basis ◽  
Decision Vector

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express a preference such as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available at http://cse.lab.imtlucca.it/~bemporad/glis.

Discontinuous and continuous transitions of collective behaviors in living systems

2021 ◽  
Author(s):  
Xu Li ◽  
Tingting Xue ◽  
Yu Sun ◽  
Jingfang Fan ◽  
Hui Li ◽  
...  
Keyword(s):  
Cognitive Abilities ◽  
Collective Motion ◽  
Finite Size ◽  
Continuous Phase ◽  
Living System ◽  
Living Systems ◽  
Finite Size Scaling ◽  
Continuous Transition ◽  
Collective Behaviors ◽  
Large Groups

Abstract Living systems are full of astonishing diversity and complexity of life. Despite differences in the length scales and cognitive abilities of these systems, collective motion of large groups of individuals can emerge. It is of great importance to seek for the fundamental principles of collective motion, such as phase transitions and their natures. Via an eigen microstate approach, we have found a discontinuous transition of density and a continuous transition of velocity in the Vicsek models of collective motion, which are identified by the finite-size scaling form of order-parameter. At strong noise, living systems behave like gas. With the decrease of noise, the interactions between the particles of a living system become stronger and make them come closer. The living system experiences then a discontinuous gas-liquid like transition of density. The even stronger interactions at smaller noise make the velocity directions of particles become ordered and there is a continuous phase transition of collective motion in addition.

scholarly journals Probabilistic models of individual and collective animal behavior

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0193049 ◽  
Author(s):  
Katarína Bod’ová ◽  
Gabriel J. Mitchell ◽  
Roy Harpaz ◽  
Elad Schneidman ◽  
Gašper Tkačik
Keyword(s):  
Animal Behavior ◽  
Probabilistic Models ◽  
Collective Animal Behavior

REDUCING DIMENSION IN GLOBAL OPTIMIZATION

2011 ◽  
Vol 08 (03) ◽  
pp. 535-544 ◽  
Author(s):  
BOUDJEHEM DJALIL ◽  
BOUDJEHEM BADREDDINE ◽  
BOUKAACHE ABDENOUR
Keyword(s):  
Global Optimization ◽  
Objective Function ◽  
Optimization Problem ◽  
Low Cost ◽  
Main Idea ◽  
Search Space ◽  
Global Optimum ◽  
Local Optima ◽  
Interesting Idea ◽  
Narrow Space

In this paper, we propose a very interesting idea in global optimization making it easer and a low-cost task. The main idea is to reduce the dimension of the optimization problem in hand to a mono-dimensional one using variables coding. At this level, the algorithm will look for the global optimum of a mono-dimensional cost function. The new algorithm has the ability to avoid local optima, reduces the number of evaluations, and improves the speed of the algorithm convergence. This method is suitable for functions that have many extremes. Our algorithm can determine a narrow space around the global optimum in very restricted time based on a stochastic tests and an adaptive partition of the search space. Illustrative examples are presented to show the efficiency of the proposed idea. It was found that the algorithm was able to locate the global optimum even though the objective function has a large number of optima.

Reliability Optimization Design of Mechanical Parts Based on Global Optimization

2012 ◽  
Vol 503-504 ◽  
pp. 40-43
Author(s):  
Ling Fang Li ◽  
Zhe Ming He ◽  
You Xin Luo
Keyword(s):  
Global Optimization ◽  
Global Solution ◽  
Optimum Design ◽  
Optimization Design ◽  
Global Optimum ◽  
Local Solution ◽  
Reliability Optimization ◽  
Mechanical Parts ◽  
Better Than

Generally, action of constraints exists in reliability optimization design of mechanical parts. Usually, general optimum methods are adopted in reliability optimization design, so the local solution is obtained. Aimed at this situation, a global optimum method was introduced by Lingo13.0 software. The result shows the model is practical and effective and its solution is global solution better than the result with general optimum method. As the method is simple, it is worthy to spread in optimum design

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