We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multiobjective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, a...

Multiobjective cellular genetic algorithms (MOcGAs) are variants of evolutionary computation algorithms by organizing the population into grid structures, which are usually 2D grids. This paper proposes a new MOcGA, namely cosine multiobjective cellular genetic algorithm (C-MCGA), for continuous multiobjective optimization. The CMCGA introduces two new components: a 3D grid structure and a cosi...

When large sensor networks are applied to the task of target tracking, it is necessary to successively identify subsets of sensors that are most useful at each time instant. Such a task involves simultaneously maximizing target detection accuracy and minimizing querying cost, addressed in this paper by the application of multi-objective evolutionary algorithms (MOEAs). The objective of maximizi...

The increasing complexity of the modern control systems has emphasized the idea of applying new approaches in order to solve design problems for different control engineering problems. This paper reports a performance comparison between traditional (linear PID controller) and evolvable methods (evolvable hardware controllers) applied to the problem of three-degrees-of-freedom manipulator contro...

This paper presents a multi-objective teaching learning algorithm based on decomposition for solving the optimal reactive power dispatch problem (ORPD). The effectiveness and performance of the proposed algorithm are compared with respect to a multi-objective evolutionary algorithm based on decomposition (MOEA/D) and the NSGA-II. A benchmark power system model is used to test the algorithms’ pe...

This paper presents a new procedure for the nondominated sorting with constraint handling to be used in a multiobjective evolutionary algorithm. The strategy uses a sorting algorithm and binary search to classify the solutions in the correct level of the Pareto front. In a problem with m objective functions, using n solutions in the population, the original nondominated sorting algorithm, used ...

This paper presents an application of elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), to efficiently schedule a set of independent tasks in a heterogeneous distributed computing system. This scheduling problem is a bi-objective problem considering two objectives. The first objective is minimization of makespan and the second one being the minimization of flowtime. As a multi-objectiv...

We compare the efficiency of the NSGA-II algorithm for the brachytherapy dose optimization problem with and without supporting solutions. A local search method enhances the efficiency of the algorithm. In comparison to a fast simulated annealing algorithm the supported hybrid NSGA-II algorithm provides much faster many non-dominated solutions. An archiving of all non-dominated solutions is usef...

The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator...

In this reach work, a well performing approach in the context of multiobjective evolutionary algorithm (MOEA) is investigated due to its complexity. This approach called NSCCGA is based upon a previously introduced approach called NSGA-II. NSCCGA performs better than NSGA-II but with a heavy load of computational complexity. Here, a novel approach called GBCCGA is introduced based on MOCCGA wit...