Bio-inspired computation: Where we stand and what's next

Ser, Javier Del; Osaba, Eneko; Molina, Daniel; Yang, Xin-She; Salcedo‐Sanz, Sancho; Camacho, David; Das, Swagatam; Suganthan, Ponnuthurai Nagaratnam; Coello, Carlos A. Coello; Herrera, Francisco

doi:10.1016/j.swevo.2019.04.008

Cited by 481 publications

(134 citation statements)

References 371 publications

(393 reference statements)

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“…In the future, we will focus our research work on the study of special cases to strengthen the algorithm in more complex conditions. We will determine how to generalize our work to handle combinatorial optimization problems and to extend DMQL-CS optimization algorithms to in the realistic engineering areas and feature selection for machine learning [102].…”

Section: Resultsmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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Section: Resultsmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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“…This article focuses on some of the most used: Ant-based algorithms [14,15], Particle Swarm Optimisation [16,17], Artificial Swarm Fish Algorithm [18], Artificial Bee Colonies [19,20] and Firefly Algorithm [21,22]. Some recent articles that analyse publications related to swarm methods show that the selected methods are among those considered in more publications [1,7]. This section includes a brief description of each method that can be completed by reading the references cited for each case.…”

Section: Swarm Algorithmsmentioning

confidence: 99%

“…Among them, bio-inspired computation includes a set of methods that attempt to imitate behaviours or phenomena observed in nature in order to solve complex problems. Swarm-based algorithms are a subset of bio-inspired methods that have gained interest in recent years as powerful methods to solve complex problems [1]. These algorithms are based on the collective behavior of self-organised and decentralised systems [2,3].…”

Section: Introductionmentioning

confidence: 99%

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A Proposal for the Organisational Measure in Intelligent Systems

Román

Delgado

2020

Applied Sciences

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The collaboration within organisations and among organisations is a fundamental concept in the attainment of the overall objectives pursued by an enterprise network in human companies. Swarm systems are intelligent systems that show collaboration within the system; moreover, some models, such as multiple ant colonies, show the collaboration of several systems to achieve a global goal. The collaboration in this type of system optimises the achievement of the overall objectives as in an enterprise network in human organisations. Being able to measure this collaboration allows establishing a relationship between the improvement in the results of the system and the degree of collaboration, both at the level of specialisation of each element of the system and the systems as a whole. The performance of a swarm system depends on the number of members in many cases, so that if we can establish a measure of specialisation and collaboration, we could tipify and classify these systems in terms of the efficiency and the realiability to perform different tasks.

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“…in terms of fitness and population diversity [9,10]), structural bias [1,11] etc. ; • statistical methods for comparing the performances of algorithms [12,13];…”

Section: Introductionmentioning

confidence: 99%

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

Caraffini¹

2020

Preprint

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The Stochastic Optimisation Software (SOS) is a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. It reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including 1) customised implementations of statistical tests, such as the Wilcoxon Rank-Sum test and the Holm-Bonferroni procedure, for comparing performances of optimisation algorithms and automatically generate result tables in PDF and LaTeX formats; 2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; 3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation operator. each testbed function. Moreover, this article comments on the current state of the literature in stochastic optimisation and highlights similarities shared by modern metaheuristics inspired by nature. It is argued that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them.

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Bio-inspired computation: Where we stand and what's next

Cited by 481 publications

References 371 publications

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

A Proposal for the Organisational Measure in Intelligent Systems

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

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