Most optimization problems naturally have several objectives, usually in conflict with each
other. The problems with two or three objective functions are referred to as Multi-Objective …

Evolutionary algorithms have been effectively used to solve multiobjective optimization
problems with a small number of objectives, two or three in general. However, when …

This paper improves a recently developed multi-objective particle swarm optimizer () that
incorporates dominance with decomposition used in the context of multi-objective …

The increased demand of Wireless Sensor Networks (WSNs) in different areas of application
have intensified studies dedicated to the deployment of sensor nodes in recent past. For …

We propose an online, multiobjective optimization (MO) algorithm to efficiently schedule the
nodes of a wireless sensor network (WSN) and to achieve maximum lifetime. Instead of …

One of the main priorities for metro line operators is the reduction of energy consumption,
due to the environmental impact and economic cost. The new moving block signalling …

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved
non-dominating sorting multiobjective genetic algorithm (NSGA-II) is two well known …

Expensive multi-objective combinatorial optimization problems have constraints in the
number of objective function evaluations due to time, financial, or resource restrictions. As …

Network coding enables higher network throughput, more balanced traffic, and securer data
transmission. However, complicated mathematical operations incur when packets are …

Many-objective optimization is very important for numerous practical applications. It,
however, poses a great challenge to the Pareto dominance based evolutionary algorithms …