Barnacles Mating Optimizer: A Bio-Inspired Algorithm for Solving Optimization Problems

Sulaiman, Mohd Herwan; Mustaffa, Zuriani; Saari, Mohd Mawardi; Daniyal, Hamdan; Daud, Mohd Razali; Razali, Saifudin; Mohamed, Amir Izzani

doi:10.1109/snpd.2018.8441097

Cited by 28 publications

(14 citation statements)

References 13 publications

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“…In the swarm intelligence algorithms are the: PSO [37,40], ant colony algorithm (ACO) [37,76], bat algorithm (BA) [38,70], and among the most recent, the reptile search algorithm (RSA) [77,78]. While in the algorithms based on organisms, one finds the algorithms of coyotes [79,80], dolphins [81,82], penguins [83,84], and moths [85,86].…”

Section: Metaheuristicsmentioning

confidence: 99%

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

et al. 2022

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Companies are constantly changing in their organization and the way they treat information. In this sense, relevant data analysis processes arise for decision makers. Similarly, to perform decision-making analyses, multi-criteria and metaheuristic methods represent a key tool for such analyses. These analysis methods solve symmetric and asymmetric problems with multiple criteria. In such a way, the symmetry transforms the decision space and reduces the search time. Therefore, the objective of this research is to provide a classification of the applications of multi-criteria and metaheuristic methods. Furthermore, due to the large number of existing methods, the article focuses on the particle swarm algorithm (PSO) and its different extensions. This work is novel since the review of the literature incorporates scientific articles, patents, and copyright registrations with applications of the PSO method. To mention some examples of the most relevant applications of the PSO method; route planning for autonomous vehicles, the optimal application of insulin for a type 1 diabetic patient, robotic harvesting of agricultural products, hybridization with multi-criteria methods, among others. Finally, the contribution of this article is to propose that the PSO method involves the following steps: (a) initialization, (b) update of the local optimal position, and (c) obtaining the best global optimal position. Therefore, this work contributes to researchers not only becoming familiar with the steps, but also being able to implement it quickly. These improvements open new horizons for future lines of research.

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Section: Metaheuristicsmentioning

confidence: 99%

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

et al. 2022

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“…In this paper, cohesive meta-heuristic is being used to solve CEELD problems and three recent algorithms have been selected to see the performance of this approach. The three algorithms are Barnacles Mating Optimizer (BMO) [9,10], Moth-Flame Optimizer (MFO) [11] and Grey Wolves Optimizer (GWO) [12]. Thus, three combination of meta-heuristic algorithms are performed viz.…”

Section: Cohesive Meta-heuristic Approachmentioning

confidence: 99%

“…BMO is the recent technique that has been proposed by [9] which is inspired by the mating behavior of barnacles in nature. In BMO, only one parameter needs to be tuned in order to obtain the acceptable results apart of number of population and maximum iteration number.…”

Section: Barnacles Mating Optimizermentioning

confidence: 99%

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Solving Combined Economic and Emission Load Dispatch Using Cohesive Meta-heuristic Approach

Sulaiman

Mustaffa

Alfandi

et al. 2021

Lecture Notes in Electrical Engineering

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This paper proposes a new approach of solving optimization problem especially in combined economic emission load dispatch (CEELD) called cohesive meta-heuristic. CEELD is one of the challenging power system operation problems which considering the minimization of cost of power generation as well as emission level simultaneously. These two objectives are contrary each other which is difficult to solve and for this paper, these two objectives are convert into a single objective by introducing weightage and cost penalty factor. Cohesive meta-heuristic on the other hand is the new terminology of cooperation of existing meta-heuristic algorithms to obtain a better solution. In order to show the effectiveness of proposed method, 40-units generators system will be utilized and compared with the other methods. KeywordsBarnacles mating optimizer • Cohesive meta-heuristic • Combined economic & emission dispatch • Grey wolves optimizer • Moth flame optimizer 1 IntroductionEconomic Load Dispatch (ELD) problem is one of the challenging tasks in power system operation. The main objective of ELD is to find the optimal combination of power generations so that the minimum cost can be obtained. Since most of the power system generations in the world still using the thermal type of generators that can eventually give bad impacts to the environmental issue, the ELD research is extended to not only to find best combination of power output that minimize the cost, but also minimize the emission to the environment, called as Economic and Emission Load Dispatch (EELD). EELD can be solve in two ways: by using multi-objective approach and combined them into single objective called Combined Economic &

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“…From the extensive literature reviews, it is evident that the application of metaheuristic algorithms into OPF solution is encouraging. Thus, this paper proposes an application of recent meta-heuristic algorithm namely Barnacles Mating Optimizer (BMO) which mimics the mating behaviour of barnacles [34][35][36] to solve OPF problem which considering the stochastic solar power generation. The contributions of this paper are as follow: a.…”

Section: Introductionmentioning

confidence: 99%

Optimal power flow with stochastic solar power using barnacles mating optimizer

Sulaiman

Mustaffa

Mohamad

et al. 2021

Int Trans Electr Energ Syst

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Summary This work proposes the implementation of recent evolutionary metaheuristic algorithm namely, barnacles mating optimizer (BMO) to solve the Optimal Power Flow (OPF) issue. BMO is inspired by the mating behaviour of barnacles which happened in two ways: by normal copulation and sperm‐cast. The effectiveness of the proposed BMO in solving the OPF is tested on a modified IEEE‐30 bus system that is integrated with solar PV farms for five cases viz (1) cost minimization of the power generation that consists of thermal and stochastic solar power generations, (2) power loss minimization, (3) voltage deviation minimization, (4) emission minimization and (5) combined cost and emission minimization of power generations. To demonstrate the effectiveness and the veracity of the solution obtained by BMO, several recent algorithms that have been reported in the literature will be utilized and compared intensively. In the end, the simulation results demonstrate that the BMO can be effectively becoming an alternative solution for the OPF issue in general.

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Barnacles Mating Optimizer: A Bio-Inspired Algorithm for Solving Optimization Problems

Cited by 28 publications

References 13 publications

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

PSO, a Swarm Intelligence-Based Evolutionary Algorithm as a Decision-Making Strategy: A Review

Solving Combined Economic and Emission Load Dispatch Using Cohesive Meta-heuristic Approach

Optimal power flow with stochastic solar power using barnacles mating optimizer

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