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Node deployment is one of the important issues to be addressed in Wireless Sensor Networks (WSNs). A proper node deployment scheme can reduce the complexity of problems in WSNs such as routing, data fusion, communication, etc. Sensors in Mobile WSNs (MWSNs) have ability to travel in the network with motion capability. This ability can extend the lifetime of WSNs by minimizing energy consumption due to travelling with optimization of deployment. This paper employs two recently proposed multi-objective optimization algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and fast and elitist genetic algorithm (NSGA-II) to find proper deployment scenarios. These multi-objective evolutionary algorithms aim at relocating mobile nodes to provide maximum sensing coverage area while minimizing the energy required for the relocation. The relocation is defined as the total travel distance of the sensors from their initial locations to their final locations. Simulation results clearly show that the non-dominated solutions have tradeoff between the travelled distance and coverage area. [ABSTRACT FROM AUTHOR]

This paper presents multiobjective fuzzy genetic algorithm optimization approach to a submersible motor design. Utilizing the concept of fuzzy sets and convex fuzzy decision making, the motor design task is formulated as a multiobjective fuzzy optimization problem and solved using a genetic algorithm. The two-dimensional Finite Element Method (FEM) is then used to confirm the validity of the optimal design. The optimization results show the effectiveness and achievement of the proposed method. [ABSTRACT FROM AUTHOR]

The majority of research on scheduling problems addresses only a single criterion while the majority prodution planning problems require the decision maker to consider more than a single criterion for making a decision. In this paper, a problem with uncertain time parameters such as processing times and due dates in the m-machine flow shop scheduling problem, which has a big importance in nowadays manufacturing systems, is considered. Further a multiobjective approach based on genetic algorithm, optimizing fuzzy makespan, fuzzy maximum tardiness and fuzzy total flow time objectives simultaneously, is developed. The algorithm is developed to obtain efficient solutions. In this algorithm the values of the multiobjective function are expressed by using membership functions. The effectiveness of the algorithm is illustrated by using small size problems. The best parameter sets of the genetic algorithm are determined by using factorial design of experiment. It is shown that the algorithm produces efficient solutions for medium and large size problems in a reasonable amount of time. [ABSTRACT FROM AUTHOR]