Numerical optimization algorithm based on genetic algorithm for a data completion problem

This work presents numerical optimization algorithm based on genetic algorithm to solve the data completion problem for Laplace’s equation. It consists of covering the missing data on the inaccessible part of the boundary from measurements on the accessible part. This problem is known to be severely ill-posed in Hadamard sense; then, regularization methods must be exploited. Metaheuristics are methods inspired by natural phenomena and which have shown their effectiveness in solving several optimization problems in different domains. Thus, adapted genetic operators for real coded genetic algorithm is proposed by formulating the problem into an optimization one. Numerical results with irregular domain are presented showing the efficiency of the proposed algorithm.Publisher's Versio

Microgrid Economic Operation Under Islanded Mode Using Charge System Search Algorithm

This paper proposes a new heuristic algorithm which is known as the charge system search algorithm (CSSA) for optimal power scheduling of islanded microgrid (MG). This technique is tested on the IEEE 33 bus test system. Also, to speed up the algorithm, a new mutation operator is designed. Results demonstrate the high efficiency of the proposed technique

Radial Distribution Network Topology Optimization Using Genetic Algorithms Considering Uncertain Load and Distributed Generation

This paper aims to study distribution network topology optimization considering uncertain load and distributed generation. Gradual increase of distributed generation in distribution network leads the network operator companies to concern more about having the best network topology, so their costs can be the lowest. MATLABTM genetic algorithms function is used to model this mathematical problem in its basic definition. A stochastic multi-objective programming algorithm is implemented and a decision maker applied to choose the best solution of non-dominated solutions set found.info:eu-repo/semantics/publishedVersio

Microgrid Economic Operation Under Islanded Mode Using Charge System Search Algorithm

This paper proposes a new heuristic algorithm which is known as the charge system search algorithm (CSSA) for optimal power scheduling of islanded microgrid (MG). This technique is tested on the IEEE 33 bus test system. Also, to speed up the algorithm, a new mutation operator is designed. Results demonstrate the high efficiency of the proposed technique

Microgrid Economic Operation Under Islanded Mode Using Charge System Search Algorithm

This paper proposes a new heuristic algorithm which is known as the charge system search algorithm (CSSA) for optimal power scheduling of islanded microgrid (MG). This technique is tested on the IEEE 33 bus test system. Also, to speed up the algorithm, a new mutation operator is designed. Results demonstrate the high efficiency of the proposed technique

Parameter Study on Weight Minimization of Network Arch Bridges

The article concerns optimization of network arch bridges. This is challenging optimization problem involving even for conventional scheme of network arch bridge the identification of some topological parameters as well as shape configurations and all sizing parameters of structural members, seeking the minimum weight. Optimal bridge scheme is sought tuning a large set of design parameters of diverse character: the type of hanger arrangement, the number of hangers, their inclination angles and placement distances, the arch shape and rise, etc. Mathematically, the optimization of the bridge scheme is a mixed-integer constrained global optimization problem solved employing stochastic evolutionary algorithm. Plane heavy/moderate/and light-deck bridges of 18, 30, 42 and 54 m spans were optimized using proposed optimization technique. The decisive design parameters and their promising ranges were revealed. Also, the influence of some simplifications is shown: changing the arch shape from elliptical to circular, placing the hangers at equal distances, etc

Application of an Improved Genetic Algorithm for Optimal Design of Planar Steel Frames

Genetic Algorithm (GA) is one of the most widely used optimization algorithms. This algorithm consists of five stages, namely population generation, crossover, mutation, evaluation, and selection. This study presents a modified version of GA called Improved Genetic Algorithm (IGA) for the optimization of steel frame designs. In the IGA, the rate of convergence to the optimal solution is increased by splitting the population generation process to two stages. In the first stage, the initial population is generated by random selection of members from among AISC W-shapes. The generated population is then evaluated in another stage, where the member that does not satisfy the design constraints are replaced with stronger members with larger cross sectional area. This process continues until all design constraints are satisfied. Through this process, the initial population will be improved intelligently so that the design constraints fall within the allowed range. For performance evaluation and comparison, the method was used to design and optimize 10-story and 24-story frames based on the LRFD method as per AISC regulations with the finite element method used for frame analysis. Structural analysis, design, and optimization were performed using a program written with MATLAB programming language. The results show that using the proposed method (IGA) for frame optimization reduces the volume of computations and increases the rate of convergence, thus allowing access to frame designs with near-optimal weights in only a few iterations. Using the IGA also limits the search space to the area of acceptable solutions

A hybrid finite element analysis and evolutionary computation method for the design of lightweight lattice components with optimized strut diameter

Components incorporating lattice structures have become very popular lately due to their lightweight nature and the flexibility that additive manufacturing offers with respect to their fabrication. However, design optimization of lattice components has been addressed so far either with empirical approaches or with the use of topology optimization methodologies. An optimization approach utilizing multi-purpose optimization algorithms has not been proposed yet. This paper presents a novel user-friendly method for the design optimization of lattice components towards weight minimization, which combines finite element analysis and evolutionary computation. The proposed method utilizes the cell homogenization technique in order to reduce the computational cost of the finite element analysis and a genetic algorithm in order to search for the most lightweight lattice configuration. A bracket consisting of both solid and lattice regions is used as a case study in order to demonstrate the validity and effectiveness of the method, with the results showing that its weight is reduced by 13.5 % when using lattice structures. A discussion about the efficiency and the implications of the proposed approach is presented

Hierarchical Representation Based Constrained Multi-objective Evolutionary Optimisation of Molecular Structures

We propose an efficient algorithm to generate Pareto optimal set of reliable molecular structures represented by group contribution methods. To effectively handle structural constraints we introduce goal oriented genetic operators to the multi-objective Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The constraints are defined based on the hierarchical categorisation of the molecular fragments. The efficiency of the approach is tested on several benchmark problems. The proposed approach is highly efficient to solve the molecular design problems, as proven by the presented benchmark and refrigerant design problems