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From Leonardo DaVinci studying bird flight to inform the design of primitive flying machines, to the sculptural organic forms embedded in the iconic architectures of Antonio Gaudi, observation of patterns in nature has overtly influenced engineers and architects over the centuries. Contemporary inspiration from observing biological structures and n...

## Context in source publication

**Context 1**

... millisecond high frequency trading of equities, securities, foreign exchange rates, and options contracts are an increasingly competitive industry among large investment banks and hedge funds, algorithmic trading in general refers to the use of mostly automated computational methods to make trading decisions related to data retrieval and analysis, signal genesis for buy or sell orders, and trade order execution. According to (Glantz & Kissel 2013) approximately 85% of all US equity trading volume in 2012 was executed in one form or another using algorithmic trading methods (Figure 3). ...

## Citations

... P best is used to store the best experience of the particle while G best is used to store the best positions among all particles (Zhang & Xie, 2009). The imagined vector diagram linking PSO equations to movement of population and particle is presented in Figure 3 ( Camilli, 2015). ...

... where i is particle's index, t the discretetime index, d the measurement being considered, n number of particles in a group, m dimensions of a particle, ω inertia weight factor, 1 r and 2 r the make random parameters, and c1, c2 the acceleration coefficient for the cognitive and social components, respectively [24]. Figure 1 shows the intellectualized vector diagram relating PSO equations to particle movement and population movement [25]. ...

The paper benevolences a hybrid metaheuristic optimization algorithm developed for the computation of Weibull probability and cumulative density functions (PDF) and (CDF) respectively. The algorithm is designed through espousing protruding topographies wich are used in the Bat algorithm (BA) and in the Particle Swarm Optimization (PSO) and is used to analyze the figure and measure constraints of Weibull Distribution (WD). The established algorithm is also verified on target test functions. The results and computational time are compared with the results obtained through the implementation of standard PSO and BA. The computed values of the parameters of WD are also verified through the calculation of, index of agreement (AI), mean absolute percentage error (MAPE) coefficient of determination (R 2) and root mean square error (RMSE). The results of parameter estimation are also compared with standard PSO and BA for the mentioned region. The comparative analysis of the results of benchmark functions and Weibull parameter estimation reveal the robustness and verstality of the developed algorithm.

... n and m represent the population of particles in a group and sizes of a particle respectively. ω, r 1 , r 2 and c 1 , c 2 are inertia weight factor, randomization parameters, and social and cognitive components, respectively [49], [50]. ...

The paper presents a modified hybrid particle swarm optimization with bat algorithm parameter inspired acceleration coefficients (MHPSO-BAAC) without and with the constriction factor to find the optimal solution of the economic dispatch problems (EDPs) incorporating conventional as well as hybrid and renewable energy sources (RESs) based plants. The algorithm is designed by modifying the recently presented hybrid PSO and BA (HPSOBA) algorithm applied for the achievement of the optimal solution of the EDPs. The modified algorithm is implemented to solve EDPs of all RESs-based power systems for three scenarios, without constraints, with time-varying demand, and with the consideration of regional load sharing dispatch (RLSD). The performance of the algorithm is also verified through the implementation of various combinations of hybrid as well as thermal power plants (TPPs). The case of TPPs consists of three different scenarios: 1) a small-scale system with constraints like ramp-rate limits (RRLs), prohibited operating zones (POZs), and power losses; 2) a medium-scale power system with consideration of emission-economic dispatch (EED); 3) a large-scale power system with valve-point loading (VPL) effect. The results of the designed MHPSO-BAAC algorithm are compared with the various metaheuristic algorithms available in the literature and the comparative analysis shows the superior performance of the developed algorithm in terms of fuel cost reduction, fast convergence, and computational time.

... where t denotes discrete-time index, i is particle's index, n is the number of particles in a group, m represents dimensions of a particle, d is the dimension being considered, ω denotes inertia weight factor, and r 1 and r 2 are randomization parameters, c 1 and c 2 are acceleration coefficients for the cognitive and social components, respectively [38]. Fig. 1 shows the conceptualized vector diagram relating PSO equations to particle movement and population movement [39]. ...

This paper presents a hybrid metaheuristic optimization algorithm developed to solve the Economic Dispatch Problem (EDP) encountered in different combinations of power plants. The algorithm is developed by assimilating the prominent features of Particle Swarm Optimization (PSO) and Bat Algorithm (BA) and improves cost reduction and convergence with lesser computational time. The developed algorithm is employed for the solution of EDP consisting of only Renewable Energy Sources (RESs) implemented at various locations in Pakistan. The all RES based EDP consists of scenarios composed of sub-scenarios having no constraints, with time-varying loads and multi-area economic dispatch (MAED). The algorithm is also tested for three different combinations of power plants, comprising of RES integrated with thermal power plants (TPPs), the small-scaled thermal power system with constraints, and a large-scaled power system with Valve Point Loading (VPL) effect. The comparative analysis of the results for the developed metaheuristic algorithm with various existing techniques shows a reasonable reduction in the cost, improved computational time, and fast convergence.