KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules

Moein, Sara; Logeswaran, Rajasvaran

doi:10.1016/j.ins.2014.02.026

Cited by 56 publications

(26 citation statements)

References 19 publications

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“…The most recent algorithms in this category are: Gravitational Search Algorithm (GSA) [30], Chemical Reaction Optimization (CRO) [31], Artificial Chemical Reaction Optimization Algorithm (ACROA) [32], Charged System Search (CSS) algorithm [33], Ray Optimization (RO) [34], Black Hole (BH) algorithm [35], Central Force Optimization (CFO) [36], Kinetic Gas Molecules Optimization algorithm (KGMO) [37], and Gases Brownian Motion Optimization (GBMO) [38].…”

Section: Literature Reviewmentioning

confidence: 99%

Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

Mirjalili

2015

Knowledge-Based Systems

3,633

1,692

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Section: Literature Reviewmentioning

confidence: 99%

Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

Mirjalili

2015

Knowledge-Based Systems

3,633

1,692

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“…The simulated result is provided in the Table 3, is developed using linearly on the shell fluid mass flow rate as follows = 0.0058. + 1.0102 (18) In this research, the LT preheater of the ORC is considered for simulation and compared it with Bell-Delaware method followed by research [16]. The different aspect was chosen for the simulation, as shown in Table 3, and correlation for the enhancement factor is chosen.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The gas molecules are the agents in the search space. Kinetic energy is used for performance measurement and control [18,19]. Considering a system with agents (gas molecules), the position of the ℎ agent is defined in the Eq.…”

Section: Kinetic Gas Molecule Optimizationmentioning

confidence: 99%

Optimization of Shell and Tube Heat Exchanger Design in Organic Rankine Cycle System Using Kinetic Gas Molecule Optimization

Reddy¹,

Reddy²

2019

IJIES

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Among various types of heat exchanger, Shell and Tube Heat Exchanger (STHX) is highly used in the industrial application. The thermal efficiency of the heat exchanger is considered as a major factor for many applications. A considerable amount of heat is present in the low-temperature geothermal heat source, which is not sufficient to generate the electricity due to its low temperature. Many researches have been made to increase the thermal efficiency of the exchanger by the using of Organic Rankine Cycles (ORC). In this research, the Kinetic Gas Molecule Optimization (KMGO) is used to find the optimized parameter settings for the power plant. The geothermal ORC system is developed in the simulation and other various factors are considered to optimize the performance. The TEMA NFN STHX with a double-segmented baffle is analyzed for single-phase flow model. The different aspects such as baffle cut, baffle leakage, etc., are given as input to the heat exchanger. The proposed technique is evaluated with two different fluids such as R245fa and R134a. The KMGO technique is based on the swarm behavior of gas molecules, which is used to provide the optimized performance. The simulated results are measured and compared it with the existing methods for validation of the performance. The outlet temperature is achieved as 64.52°C and the enhancement factor is achieved as 1.88 for the R245fa fluid in STHX. This is achieved due to the KMGO technique identifies the global minima effectively due to the kinetic gas molecules theory, which shows the high efficiency compared to the existing method.

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“…Over the last two decades, swarm-based algorithms have attracted more and more attention of researchers, and numerous new swarm-based algorithms have been developed (see Refs. [27][28][29][30][31][32][33][34][35], such as particle swarm optimization (PSO), 2 ant colony optimization (ACO), 3 arti¯cial bee colony (ABC), 4 arti¯cial¯sh swarm algorithm (AFSA), 5 cuckoo search (CS), 11 bat algorithm (BA), 13 wolf search algorithm (WSA), 14 swallow swarm optimization algorithm (SSO), 15 kinetic energy of gas molecules (KGMO), 16 animal migration optimization (AMO), 17 magnetic optimization algorithm (MOA), 18 ions motion algorithm (IMO), 19 grey wolf optimizer (GWO), 20 ant lion optimizer (ALO), 21¯r e°y algorithm (FA), 22 krill herd (KH), 23 chicken swarm optimization (CSO), 25 ray optimization (RO), 32 black hole (BH), 34 etc. These algorithms mostly mimic the social and individual behavior of swarm, herds, schools, or groups of creatures in nature, or simulate the natural or physical phenomena in the universe.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, di®erent kinds of the physics-inspired algorithms have been proposed, such as KGMO, 16 MOA, 18 IMO, 19 gravitational search algorithm (GSA), 31 RO, 32 states of matter search (SMS), 33 BH, 34 arti¯cial chemical reaction optimization (ACROA), 35 etc. Similar to the previous two classes, optimization is performed by a cluster of search agents (called candidate solutions).…”

Section: Introductionmentioning

confidence: 99%

A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

Wang

Zhang

et al. 2016

Int. J. Comp. Intel. Appl.

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A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and arti¯cial bee colony (ABC), in the terms of the ability to¯nd the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.

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KGMO: A swarm optimization algorithm based on the kinetic energy of gas molecules

Cited by 56 publications

References 19 publications

Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm

Optimization of Shell and Tube Heat Exchanger Design in Organic Rankine Cycle System Using Kinetic Gas Molecule Optimization

A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

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