The grasshopper algorithm (GOA) is a recent algorithm. It widely used in many applications and results good solution. simple the accuracy very high. GOA has some limitations due to use of linear comfort zone parameter that causes difficulties balancing between exploration exploitation which may lead fall local optimum. In this paper modification made improve operation GOA. A nonlinear function ...

We proposes an improved grasshopper algorithm for global optimization problems. Grasshopper (GOA) is a recently proposed meta-heuristic inspired by the swarming behavior of grasshoppers. The original GOA has some drawbacks, such as slow convergence speed, easily falling into local optimum, and so on. To overcome these shortcomings, we based on logistic Chaos maps opposition-based learning strat...

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA has been successfully applied to solve various optimization problems several domains demonstrated its merits literature. This paper proposes comprehensive review based on more than 120 scientific articles published leading publishers...

this paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. this paper presen...

in distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. in this paper, the hybrid shuffled frog leaping algorithm (hsfla) is used to optimize balanced and unbalanced radial distribution systems by means of a network reconfiguration and capacitor placement. high accuracy and ...

estimation of parameters of a hydrologic model is undertaken using a procedure called “calibration” in order to obtain predictions as close as possible to observed values. this study aimed to use the particle swarm optimization (pso) algorithm for automatic calibration of the hec-hms hydrologic model, which includes a library of different event-based models for simulating the rainfall-runoff pr...

Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand time-cost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall ...

Many-objective optimization problems (MaOPs) are challenging in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides objective space into projection plane free dimension(s). The balance between conver...

This paper focuses on multi-objective designing of multi-machine Thyristor Controlled Series Compensator (TCSC) using Strength Pareto Evolutionary Algorithm (SPEA). The TCSC parameters designing problem is converted to an optimization problem with the multi-objective function including the desired damping factor and the desired damping ratio of the power system modes, which is solved by a SPEA ...