Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve maximum power output minimum wind cost. This may be accomplished by applying single or multi-objective techniques. this paper, we apply objective hill-climbing algorithm (HCA) three evolutionary algorithms (NSGA-II, SPEA2 PESA-II) well-known bench...

A new evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine benchmark multiobjective optimization problems, and shown to produce non-dominated solutions with significant diversity, outperforming state-of-the-art multi-objective evolutionary algorithms viz., Non-dominated Sorting Genetic Algorithm – II (NSGA-II), Strength Pareto Evolutionary algorithm II (SPEA-II) and P...

Cooperative coevolutionary evolutionary algorithms differ from standard evolutionary algorithms’ architecture in that the population is split into subpopulations, each of them optimising only a sub-vector of the global solution vector. All subpopulations cooperate by broadcasting their local partial solutions such that each subpopulation can evaluate complete solutions. Cooperative coevolution ...

In this work we give an extension of Kauffman’s NKLandscapes to multiobjective MNK-Landscapes in order to study the effects of epistasis on the performance of multiobjective evolutionary algorithms (MOEAs). This paper focuses on the development of multiobjective random one-bit climbers (moRBCs). We incrementally build several moRBCs and analyze basic working principles of state of the art MOEAs...

A new multi-objective evolutionary algorithm which can be applied to many nonlinear multi-objective optimization problems is proposed. Its aim is to obtain a discrete fixed size set approximating the complete Pareto-front quickly. It adapts ideas from different multiand single-objective optimization evolutionary algorithms, although it also incorporates new devices, namely, a new method to impr...

In this paper the use of a powerful single-objective optimization methodology in Multi-objective Optimization Algorithms (MOEAs) is introduced. The Flexible Evolution concepts (FE) have been recently developed and proved its efficiency gains compared with several Evolutionary Algorithms solving single-objective challenging problems. The main feature of such concepts is the flexibility to self-a...

In Wireless Sensor Network (WSN), coverage and connectivity are the vital challenges in target-based region. The linear objective is to find positions cover complete target nodes between each sensor for data forwarding towards base station given a grid with points potential placement position. this paper, multiobjective problem on WSN (t-WSN) derived, which minimizes number of deployed n...

Voltage stability has become an important issue in planning and operation of many power systems. Contingencies such as unexpected line outages in a stressed system may often result in voltage instability, which may lead to voltage collapse. This paper presents evolutionary algorithm techniques like Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm II (NSGA II) approach for solv...

In this paper, we present the first proposal to use a cultural algorithm to solve multiobjective optimization problems. Our proposal uses evolutionary programming, Pareto ranking and elitism (i.e., an external population). The approach proposed is validated using several examples taken from the specialized literature. Our results are compared with respect to the NSGA-II, which is an algorithm r...

Grid facilitates global computing infrastructure for user to consume the services over the network. To optimize the workflow grid execution, a robust multi-objective scheduling algorithm is needed. In this paper, we considered three conflicting objectives like execution time (makespan), total cost and reliability. We propose a multi-objective scheduling algorithm, using R-NSGA-II approach based...