Rashida Adeeb Khanum

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JADE is a recent adaptive version of nature inspired algorithm, Differential Evolution (DE). It has shown considerable performance improvement on a set of well studied benchmark functions. This paper studies whether the performance of JADE can be improved further by changing its random population initialization with centroid based population initialization.(More)
In the last two decades, multiobjective optimization has become mainstream because of its wide applicability in a variety of areas such engineering, management, the military and other fields. Multi-Objective Evolutionary Algorithms (MOEAs) play a dominant role in solving problems with multiple conflicting objective functions. They aim at finding a set of(More)
Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different(More)
Penalty functions are frequently used for dealing with constraints in constrained optimization. Among different types of penalty functions, dynamic and adaptive penalty functions seem effective, since the penalty coefficients in them are adjusted based on the current generation number (or number of solutions searched) and feedback from the search. In this(More)
Recently, we proposed a new threshold based penalty function. The threshold dynamically controls the penalty to infeasible solutions. This paper implants the two different forms of the proposed penalty function in the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multiobjective optimization problems.(More)
This paper proposes a Parallel Guided Local Search (PGLS) framework for continuous optimization. In PGLS, several guided local search (GLS) procedures (agents) are run for solving the optimization problem. The agents exchange information for speeding up the search. For example, the information exchanged could be knowledge about the landscape obtained by the(More)
This paper compares the performance of our recently proposed threshold based penalty function against its dynamic and adaptive variants. These penalty functions are incorporated in the update and replacement scheme of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multiobjective optimization problems(More)
Multi-objective evolutionary algorithm (MOEAs) are well established population based stochastic techniques. They employ various evolutionary operators and approximate a set of optimal solutions for the problem at hand in single run unlike traditional mathematical programing. Each search operator have certain benefits and limitations. A multiple use of(More)