This paper describes a novel method for attacking constrained optimisation problems with evolutionary algorithms, and demonstrates its effectiveness over a range of problems. COMOGA (Constrained Optimisation by MultiObjective Genetic Algorithms) combines two evolutionary techniques for multiobjective optimisation with a simple regulatory mechanism to produce a constrained optimisation method. I...

Geometric programming is an important class of optimization problems that enable practitioners to model a large variety of real-world applications, mostly in the field of engineering design. In many real life optimization problem multi-objective programming plays a vital role in socio economical and industrial optimizing problems. In this paper we have discussed the basic concepts and principle...

Xiaogang Wu 1,2,*, Wenwen Shi 1,3 ID and Jiuyu Du 3 1 College of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China; [email protected] 2 State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China 3 State Key Laboratory of Automotive Safety and Energy, Tsinghua Unive...

for multi-objective optimal reactive power dispatch (morpd), a new approach is proposed where simultaneous minimization of the active power transmission loss, the bus voltage deviation and the voltage stability index of a power system are achieved. optimal settings of continuous and discrete control variables (e.g. generator voltages, tap positions of tap changing transformers and the number of...

The coupling of performance functions due to common design variables and uncertainties in an engineering design process will result in difficulties in optimization design problems, such as poor collaboration among design objectives and poor resolution of design conflicts. To handle these problems, a fuzzy interactive multi-objective optimization model is developed based on Pareto solutions, whe...

We propose a new iterative search procedure for the numerical treatment of unconstrained multi-objective optimization problems (MOPs) which steers the search along a predefined direction given in objective space. Based on this ideawewill present twomethods: directed search (DS) descent which seeks for improvements of the given model, and a novel continuation method (DS continuation) which allow...

We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the e$cient set of such problem. In order to deal with large-scale problems, we transform this approach to propose an interactive procedure. The method is tested on the multi-objective knapsack problem and the multi-objective assig...

Vehicle Routing Problem (VRP) is a NP-Complete and a multi-objective problem. The problem involves optimizing a fleet of vehicles that are to serve a number of customers from a central depot. Each vehicle has limited capacity and each customer has a certain demand. Genetic Algorithm (GA) maintains a population of solutions by means of a crossover and mutation operators. We propose new methods f...

Although the regularisation increased the popularity of inverse analysis due to its capability of deriving a stable solution, the significant problem is that the solution depends upon the regularisation parameters chosen. This paper presents a technique for deriving solutions without the use of the parameters, and further an optimisation method, which can work efficiently for problems of concer...

The Cuckoo optimization algorithm (COA) is developed for solving single-objective problems and it cannot be used for solving multi-objective problems. So the multi-objective cuckoo optimization algorithm based on data envelopment analysis (DEA) is developed in this paper and it can gain the efficient Pareto frontiers. This algorithm is presented by the CCR model of DEA and the output-oriented a...