Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization

An, Jing; Kang, Qi; Wang, Lei; Wu, Qidi

doi:10.1007/s12559-012-9189-5

Cited by 43 publications

(21 citation statements)

References 29 publications

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“…firefly algorithm (FA) [60], the great salmon run (TGSR) [61], mutable smart bee algorithm (MSBA) [62], and mussels wandering optimization (MWO) [63], are scrutinized.…”

Section: The Considered Bic Algorithmsmentioning

confidence: 99%

“…Proposed by An et al [63], MWO is a recently proposed BIC based metaheuristic optimization technique which is implemented based on the bed formatting behavior of mussels. Mussels have an intention towards forming beds of varying density at soft surfaces, especially sea shores.…”

Section: Mussels Wandering Optimizationmentioning

confidence: 99%

“…During the optimization procedure, a set of smart bees, 5 in our case, are selected and are fed to the mutation phase for further position updating [68]. For MWO, value of the shape parameter of the levy flight of 1.8, short range reference coefficient of 0.11, long range reference coefficient of 0.75, moving coefficient of 0.63, 1.26 and 1.05, walking scale of 0.1, Levy distribution of 1.5, and scale factor of space of 0.5 are selected [63]. For lower-level training of the soft TDNN sensor, sigmoid transformation function, fully complex architecture, Levenberg-Marquardt training method, epochs number of 100, learning rate of 0.1, and the range of weights and thresholds of [-1,1] are selected [69].…”

Section: Parameter Selection and Performance Metrics Verificationmentioning

confidence: 99%

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Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Azad

Mozaffari

2015

Meccanica

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In this paper, an attempt is made to develop a bio-inspired soft sensor as a real-time identifier for online estimations of the engine-out hydrocarbon emission and exhaust gas temperature over the coldstart operation of an automotive engine. To date, a number of studies have been done to design soft models as offline identifiers for capturing the knowledge of a set of collected coldstart databases. The results indicated that bio-inspired computation can be of great use for the identification of coldstart phenomenon. It also can be a good alternative to classic approaches. However, bio-inspired soft sensors have not been used as online estimators for monitoring the behavior of coldstart phenomenon. This is while it is desirable to have a flexible, inexpensive online identifier which can be employed to detect the realtime behavior of the engine over coldstart periods.Here, an exhaustive numerical study is conducted to demonstrate the potentials of bio-inspired approaches for online measuring of engine-out hydrocarbon emission and exhaust gas temperature during the coldstart operation. To do so, a set of well-known bioinspired optimizers are combined with a time-delayed artificial neural network to design an evolvable soft sensor. The results clearly demonstrate the high potential of bio-inspired computation for handling the difficulties associated with the real-time identification of engine-out hydrocarbon emission and exhaust gas temperature.

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“…firefly algorithm (FA) [60], the great salmon run (TGSR) [61], mutable smart bee algorithm (MSBA) [62], and mussels wandering optimization (MWO) [63], are scrutinized.…”

Section: The Considered Bic Algorithmsmentioning

confidence: 99%

Section: Mussels Wandering Optimizationmentioning

confidence: 99%

Section: Parameter Selection and Performance Metrics Verificationmentioning

confidence: 99%

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Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Azad

Mozaffari

2015

Meccanica

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“…Optimization studies enable scientists to find the optimal solution for a certain problem across a wide variety of disciplines [1][2][3][4]. For example, the optimization of electrical contact materials for particular applications is crucial for the reliable operation of relays and contactors [5].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Mechanical Alloying Parameters of Cu25W Electrical Contact Material

Biyik

Aydın

2017

Acta Phys. Pol. A

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In this study, the effect of mechanical alloying parameters, namely the effect of process control agent, ballto-powder weight ratio and milling duration, on the synthesis of Cu25W composite powder was investigated. Planetary-type ball milling equipment was used to conduct mechanical alloying experiments. Stearic acid was used as the process control agent in order to establish a balance between cold welding and fracturing. The optimum amount of stearic acid was determined as a function of particle size and milling time at constant speed. By using this optimum amount of process control agent, three different ball-to-powder weight ratio values were also employed, and the effect of ball-to-powder weight ratio on particle size and morphology of Cu25W composite powders was investigated. The microstructural evolution of the milled powders was characterized using scanning electron microscopy and laser diffraction analysis. The test results have shown that the morphology and particle size distribution of the milled powders change significantly depending upon the milling parameters. In addition, higher ball-to-powder weight ratio values tend to lower the milling duration for the same amount of particle size reduction. However, particle size reduction suffers beyond the maximal value of ball-to-powder weight ratio, especially in the later stages of mechanical alloying.

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“…Useful analogies with the way vertebrate organisms defend themselves against pathogenic in microbes led to the development of Artificial Immune Systems [8,9]. The bio-inspired list of contributions continues to grow: cellular automata can perform computation relying on simple competition for resources rules [10,11]; Ant Colony optimization emulates how ants find food and leave a pheromone trace back to the solution [12]; Mussels Wandering optimization was inspired ecologically by mussels behavior when forming bed patterns in their habitat [13]; Particle Swarm Optimization (PSO) started as a representation of social knowledge embedded in movements inside flocks of birds [14]; etc. Among these strategies for brain-inspired computing, emulations of how the cognition process itself works inside the most complex organ of all, the brain, is one of the most important studies for both its epistemological and practical value [15].…”

Section: Introductionmentioning

confidence: 99%

Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm

Khan

Xue

et al. 2015

Cogn Comput

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The self-organizing map (SOM) approach has been used to perform cognitive and biologically inspired computing in a growing range of cross-disciplinary fields. Recently, the SOM based neural network framework was adapted to solve continuous derivative-free optimization problems through the development of a novel algorithm, termed SOM-based optimization (SOMO). However, formal convergence questions remained unanswered which we now aim to address in this paper. Specifically, convergence proofs are developed for the SOMO algorithm using a specific distance measure. Numerical simulation examples are provided using two benchmark test functions to support our theoretical findings, which illustrate that the distance between neurons decreases at each iteration and finally converges to zero. We also prove that the function value of the winner in the network decreases after each iteration. The convergence performance of SOMO has been benchmarked against the conventional particle swarm optimization algorithm, with preliminary results showing that SOMO can provide a more accurate solution for the case of large population sizes.

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Mussels Wandering Optimization: An Ecologically Inspired Algorithm for Global Optimization

Cited by 43 publications

References 29 publications

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

Optimization of Mechanical Alloying Parameters of Cu25W Electrical Contact Material

Convergence Analysis of a New Self Organizing Map Based Optimization (SOMO) Algorithm

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