On the Development of a Novel Smell Agent
    Optimization (Sao) for Optimization Problems

Salawudeen, Ahmed Tijani; Mu’azu, Muhammed Bashir; Sha’aban, Yusuf A.; Adedokun, Emmanuel Adewale

doi:10.26634/jpr.5.4.15677

Cited by 6 publications

(10 citation statements)

References 24 publications

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“…where N is the total hours considered, TAC is the total annual cost, AMC is the annual maintenance cost and ACC is the annual capital cost. The annual maintenance cost is expressed as: AMC ¼ n pv P pvm þ n wt P wtm (13) Whereas, the total capital cost is calculated as:…”

Section: Objective Function Formulationmentioning

confidence: 99%

See 1 more Smart Citation

Improved Smell Agent Optimization Sizing Technique Algorithm for a Grid-Independent Hybrid Renewable Energy System

Biliyok¹,

Tijani²

2023

Renewable Energy - Recent Advances

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This chapter discuss an improvement on the novel computational intelligent algorithm using the smell phenomenon. In the standard smell agent optimization algorithm, the olfactory capacity is constant thereby assuming that every smell agent has the same sensing capacity. In the improved smell agent optimization algorithm, that is changed to account for the difference in smell agent capacity. The algorithm was run against the standard smell agent optimization on Matlab to find the best HRES design using annual cost, Levelized cost of electricity (LCE), loss of power supply probability (LPSP) and excess energy. It was shown after the comparative analysis that there was a 79%, 99.9% and 53.4% improvement for annual cost, LCE and LPSP respectively. Statistically, results showed that the iSAO obtained the most cost effective HRES design compared to the benchmarked algorithms.

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Section: Objective Function Formulationmentioning

confidence: 99%

“…iii. The smell source could be more than one and each source evaporate the same number of smell molecules with varying concentration [13].…”

Section: Important Assumptionsmentioning

confidence: 99%

Improved Smell Agent Optimization Sizing Technique Algorithm for a Grid-Independent Hybrid Renewable Energy System

Biliyok¹,

Tijani²

2023

Renewable Energy - Recent Advances

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“…At every stage in the optimization process, the best information flow position is recorded as the agent whose position is required for trailing purpose. Detail information about SAO implementation can be found in [32,33].…”

Section: Smell Agent Optimization (Sao)mentioning

confidence: 99%

FANET optimization: a destination path flow model

Sadiq

Salawudeen

2020

IJECE

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Closed-loop routing in flying ad hoc networks (FANET) arises as a result of the quick changes of communication links and topology. As such, causing link breakage during information dissemination. This paper proposed a destination path flow model to improve the communication link in FANET. The models utilized Smell Agent Optimization and Particle Swarm Optimization algorithms in managing link establishment between communicating nodes. The modeled scenario depicts the practical application of FANET in media and sports coverage where only one vendor is given the license for live coverage and must relay to other vendors. Three different scenarios using both optimization Algorithms were presented. From the result obtained, the SAO optimizes the bandwidth costs much better than PSO with a percentage improvement of 10.46%, 4.04% and 3.66% with respect to the 1st, 2nd and 3rd scenarios respectively. In the case of communication delay between the FANET nodes, the PSO has a much better communication delay over SAO with percentage improvement of 40.89%, 50.26% and 68.85% in the first, second and third scenarios respectively.

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“…Consequently, higher exploitation often leads to premature convergence, while higher exploration causes slow convergence [ 19 ]. An optimal solution to real engineering optimization problems requires balancing exploitation and exploration [ 20 ]. Recently, metaheuristic algorithms have been modified or hybridized to optimize some engineering optimization problems effectively.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the modes provide an agent with a unique optimization approach and preventive measures for being trapped in local minima. Precisely, the interlinked SAO modes initiate multiple solutions, track the path, and detect the best optimal solution [ 19 , 20 , 35 ]. Thus, the technique mimics the static and dynamic control characteristics, regenerating multiple solutions to solve a complex problem [ 24 ] optimally.…”

Section: Introductionmentioning

confidence: 99%

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations

Mohammed¹,

Sha’aban

Umoh

et al. 2023

PLoS ONE

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This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

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On the Development of a Novel Smell Agent Optimization (Sao) for Optimization Problems

Cited by 6 publications

References 24 publications

Improved Smell Agent Optimization Sizing Technique Algorithm for a Grid-Independent Hybrid Renewable Energy System

Improved Smell Agent Optimization Sizing Technique Algorithm for a Grid-Independent Hybrid Renewable Energy System

FANET optimization: a destination path flow model

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations

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