Adaptive firefly algorithm: parameter analysis and its application

Freezing firefly algorithm for efficient planted (ℓ, d) motif search

The detection of inimitable patterns (motif) occurring in a set of biological sequences could elevate new biological discoveries. Its application in recognition of transcription factors and their binding sites have demonstrated the necessity to attain knowledge of gene function, human diseases, and drug design. The literature identifies (ℓ, d) motif search as the widely studied problem in PMS (Planted Motif Search). This paper proposes an efficient optimization algorithm named "Freezing FireFly (FFF)" to solve (ℓ, d) motif search problem. The new strategy freezing such as local and global was added to increase the performance of the basic Firefly algorithm. It freezes the best possible out coming positions even in the lesser brighter one. The performance of the proposed algorithm is experienced on simulated and real datasets. The experimental results show that the proposed algorithm resolves the instance (50, 21) within 1.47 min in the simulated dataset. For real (such as ChIP-seq (Chromatin Immunoprecipitation)) and synthetic datasets, the proposed algorithm runs much faster in comparison to existing state-of-the-art optimization algorithms, including Samselect, TraverStringRef, PMS8, qPMS9, AlignACE, FMGA, and GSGA.

Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems

In this study, a standalone hybrid wind turbine (WT)/photovoltaic (PV)/biomass/pump-hydro-storage energy system was designed and optimized based on technical, economic, and environmental parameters to provide the load demand with an objective function of minimum cost of energy (COE). The constraints of the proposed approach are the loss of power supply probability, and the excess energy fraction. The proposed approach allows the combination of different sources of energy to provide the best configuration of the hybrid system. Therefore, the proposed system was optimized and compared with a WT/PV/biomass/battery storage-based hybrid energy system. This study proposes three different optimization algorithms for sizing and minimizing the COE, including the whale optimization algorithm (WOA), firefly algorithm (FF) and particle swarm optimization (PSO) and the optimization procedure was executed using MATLAB software. The outcomes of these algorithms are contrasted to select the most effective, and the one providing the minimum COE is chosen based on statistical analysis. The results indicate that the proposed hybrid WT/PV/biomass/pump-hydro storage energy system is environmentally and economically practical. Meanwhile, the outcomes demonstrated the technical feasibility of a pump-hydro energy storage system in expanding the penetration of renewable energy sources compared to other existing systems. The COE of the pumped-hydro storage hybrid system was found to be lower (0.215 $/kWh) than that with batteries storage hybrid system (0.254 $/kWh) which was determined using WOA at the same load demand.

Self-Adaptive Firefly Algorithm with Pole Zero Cancellation Method for Controlling SMIB Power System

The accurate analysis and controller design for high-order systems are important issues. In this paper, a design of controller is suggested to Single Machine Infinite Bus (SMIB) System in which its order is reduced by the proposed self-adaptive Firefly Algorithm (FA). First, the dynamic adaption to the dominant parameters of the standard FA is proposed for overcoming its disadvantages and improving its ability in searching the global optimum solution with application to reduce the model order of SMIB. The parameters which are proposed to self-adaption are both of the light absorption based on the mean distance of fireflies’ positions and the step setting based on the fitness information to the status of preceding firefly and the current fireflies. Second, proportional–integral–derivative (PID) controller is suggested to be designed by pole zero cancellation method via the step response characteristics of the reduced-order SMIB to be applied to its high order. The efficiency of the proposed methods is tested on high-order SMIB to get its corresponding low order and to control it. The experimental results prove the efficacy of self-adaptive FA compared to the standard FA and the methods in the literature in terms of step response characteristics using error indices. The proposed methods assure the stability of the reduced order and the ability of the designed controller to handle both high and low-order model.

The Enhanced Firefly Algorithm Based on Modified Exploitation and Exploration Mechanism

As a nature-inspired search algorithm, the Firefly algorithm (being a naturally outstanding search algorithm with few control parameters) may have a considerable influential performance. In this paper, we present a new firefly algorithm to address the parameter selection and adaptation strategy in the standard firefly algorithm. The proposed firefly algorithm introduces a modified exploration and exploitation mechanism, with adaptive randomness and absorption coefficients. The proposed method employs the adaptation of the randomness and absorption coefficients to be a function of time/iterations. Moreover, gray relational analysis advancing fireflies is used to allocate different information from appealing ones effectively. Standard benchmark functions are applied to verify the effects of these improvements and it is illustrated that, in most situations, the performance of the proposed firefly algorithm is superior to (or at least highly competitive with) the standard firefly algorithm, and state-of-the-art approaches in terms of performance.