Hybrid Big Bang-Big Crunch Algorithm Research Articles

Structural Optimization of Ship Lock Heads during Construction Period considering Concrete Creep

Traditional structural optimization is mainly based on the assumption that the materials are elastic, which cannot represent real stress fields in structures. In this study, the genetic algorithm, big bang-big crunch algorithm, and hybrid big bang-big crunch algorithm were employed to optimize the design factors of ship lock heads during concrete construction. The optimization goal was to determine the minimum volume of concrete. The factors considered included the hydration heat, the early-stage creep, and the transient deformation under external loads. In the finite element analysis, three types of boundary conditions were considered. The whole construction process was simulated, and the maximum tensile and compressive stresses, the stability, and the overturning of the lock head were examined. Based on the finite element analysis, to reduce the consumption of memory, a set of implicit recursive equations were used to calculate the thermal creep stress. Thirty-four design variables were distinguished for optimization. A case study on the optimization of a ship lock head was used to demonstrate the optimization process. The optimization results showed that the hybrid big bang-big crunch algorithm was more effective, and some conclusions were derived.

Hydrothermal Economic Dispatch Using Hybrid Big Bang-Big Crunch (HBB-BC) Algorithm

In power generation, it is expected to generate required amount of power based on the load system with minimum cost which is generally called as economic dispatch (ED). Therefore, providing an approach which can be used to solve ED problem in electric systems for effective operation is important. In this paper, hybrid big bang-big crunch (HBB-BC) algorithm was proposed to solve hydrothermal ED problem. Basically, HBB-BC is an algorithm which combined between BB-BC and PSO that use the PSO to improve ability of BB-BC algorithm. To examine the performance of HBB-BC algorithm for hydrothermal ED, IEEE 26 bus was utilized as test system with 6 generators (3 thermal and 3 hydro generator units). Next, simulation output was compared with improved PSO, and modified improved PSO algorithm. From results, HBB-BC algorithm shown the best result under tested case. It confirmed by obtained total operating cost for HBB-BC algorithm was the lowest (11,344.593 $/h) compared to IPSO (11,451.001 $/h) and MIPSO algorithms (11,387.000 $/h).

Coordinated optimization and control of SFCL and SMES for mitigation of SSR using HBB-BC algorithm in a fuzzy framework

Abstract Increasing demand for electrical power and expensive expansion of power systems to meet this demand leads the planners to find techno-economical solutions to overcome these challenges. Using series capacitors as reactive power compensation devices in a long transmission lines is a well-known and suitable way to face these challenges in power systems. Transmitted electrical power and the stability margin of power systems are economically improved by the proposed series devices. However, employing the series capacitors in long transmission lines causes a well-known problem in power systems as Sub Synchronous Resonance (SSR). In the present work, the mitigation of SSR is formulated as a Multi Objective Problem (MOP) in a fuzzy framework by using optimal coordinated control of Superconducting Fault Current Limiter (SFCL) and Superconducting Magnetic Energy Storage (SMES). Objective functions of MOP includes the minimization of the initial energy stored in the SMES unit, the energy loss of SFCL, the kinetic energy in generator rotor, the active power deviations in faulted bus, and the rotor speed deviations. These five objective functions are scaled by a fuzzy operator, then the scaled objective functions are aggregated by the “max-geometric mean” operator to generate the multi objective function. To solve the proposed multi-objective problem, the Hybrid Big Bang-Big Crunch (HBB-BC) as a meta-heuristic optimization algorithm is used. In order to evaluate the efficiency of the proposed algorithm, it is implemented on the IEEE First Benchmark Model (FBM) for SSR studies. According to the simulation results of the present study, the proposed algorithm is more effective in reducing the SSR problem in comparison with other algorithms.

A hybrid Big Bang-Big Crunch algorithm for energy optimization on heterogeneous distributed system

A hybrid algorithm is presented for global optimization of the energy consumption rather than makespan of the system. The cost profile is optimized by extending the execution time of the tasks on dynamic voltage scalable processing elements in embedded environment with meeting the execution constraints. The modified Big Bang Big Crunch (BBBC) method improves the scheduling efficiency of the tasks by simulating one of the theories of the evolution of the universe. BBBC algorithm generates disorder data points inspired by energy dissipation procedure of the Big Bang phase, and moves those data points to a single representative data point inspired by a center of mass cost approach in the Big Crunch phase. The Logistic and Sinusoidal chaotic maps are investigated and utilized to improve the movement step of the BBBC algorithm. The proposed hybrid algorithm is tested on several benchmark data sets and its performance is compared with those of Ant Colony Optimization and HEFT strategies. The simulation experiment shows that the presented evolutionary optimization algorithm is robust and suitable for energy saving.

Voltage and frequency regulation in autonomous microgrids using Hybrid Big Bang-Big Crunch algorithm

This paper proposes an optimal power control strategy for inverter-based Distributed Generation (DG) units in autonomous microgrids. It consists of power, voltage, and current controllers with Proportional-Integral (PI) regulators. The droop concept is used for the power control strategy. Static parameters in PI regulators may not ensure the most optimal solution due to inevitable changes happening in microgrid configuration and loads. In the proposed method, after occurring a load change in a standalone microgrid, parameters of the PI controller are dynamically adjusted to get the most optimal operating point that satisfies objective functions. The optimization problem is formulated as a multi-objective programming with objective functions of minimizing overshoot/undershoot, settling time, rise time, and Integral Time Absolute Error (ITAE) in the output voltage. These objective functions are combined using fuzzy memberships. The Hybrid Big Bang-Big Crunch algorithm (HBB-BC) is used to solve the optimization problem. The proposed methodology is simulated on a case study and according to obtained results, the suggested tuning of PI parameters leads to a better voltage response than previous methods. The case study is also solved using the Particle Swarm Optimization (PSO) and Big Bang-Big Crunch (BB-BC) algorithms and it is found that the HBB-BC gives a better solution than the PSO and BB-BC.

Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems

In this paper, a multi-objective framework is proposed for simultaneous optimal network reconfiguration and DG (distributed generation) power allocation. The proposed method encompasses objective functions of power losses, voltage stability, DG cost, and greenhouse gas emissions and it is optimized subject to power system operational and technical constraints. In order to solve the optimization problem, the HBB-BC (Hybrid Big Bang-Big Crunch) algorithm as one of the most recent heuristic tools is modified and employed here by introducing a mutation operator to enhance its exploration capability. To resolve the scaling problem of differently-scaled objective functions, a fuzzy membership is used to bring them into a same scale and then, the fuzzy fitness of the final objective function is utilized to measure the satisfaction level of the obtained solution. The proposed method is tested on balanced and unbalanced test systems and its results are comprehensively compared with previous methods considering different scenarios. According to results, the proposed method not only offers an enhanced exploration capability but also has a better converge rate compared with previous methods. In addition, the simultaneous network reconfiguration and DG power allocation leads to a more optimal result than separately doing tasks of reconfiguration and DG power allocation.

CO 2 and cost optimization of reinforced concrete footings using a hybrid big bang-big crunch algorithm

A procedure is developed for the design of reinforced concrete footings subjected to vertical, concentric column loads that satisfies both structural requirements and geotechnical limit states using a hybrid Big Bang-Big Crunch (BB-BC) algorithm. The objectives of the optimization are to minimize cost, CO $_{2}$ emissions, and the weighted aggregate of cost and CO $_{2}$ . Cost is based on the materials and labor required for the construction of reinforced concrete footings and CO $_{2}$ emissions are associated with the extraction and transportation of raw materials; processing, manufacturing, and fabrication of products; and the emissions of equipment involved in the construction process. The cost and CO $_{2}$ objective functions are based on weighted values and are subjected to bending moment, shear force, and reinforcing details specified by the American Concrete Institute (ACI 318-11), as well as soil bearing and displacement limits. Two sets of design examples are presented: low-cost and low-CO $_{2}$ emission designs based solely on geotechnical considerations; and designs that also satisfy the ACI 318-11 code for structural concrete. A multi-objective optimization is applied to cost and CO $_{2}$ emissions. Results are presented that demonstrate the effects of applied load, soil properties, allowable settlement, and concrete strength on designs.