This paper presents a novel surrogate-assisted evolutionary algorithm, CSMOEA, for multi-objective optimization problems (MOPs) with computationally expensive objectives. Considering most algorithms (SAEAs) do not make full use of population information and only in either the objective space or design independently, to address this limitation, we propose new strategy comprehensive utilization s...

Multiobjective optimization problems have been widely addressed using evolutionary computation techniques. However, when dealing with more than three conflicting objectives (the so-called many-objective problems), the performance of such approaches deteriorates. The problem lies in the inability of Pareto dominance to provide an effective discrimination. Alternative ranking methods have been su...

A core challenge ofMultiobjective Evolutionary Algorithms (MOEAs) is to attain evenly distributed Pareto optimal solutions along the Pareto front. In this paper, we propose a novel asymmetric Pareto-adaptive (apa) scheme for the identification of well distributed Pareto optimal solutions based on the geometrical characteristics of the Pareto front. The apa scheme applies to problem with symmetr...

55 Multi-Objective Optimization of Squeeze Casting Process using Evolutionary Algorithms Manjunath Patel G C, Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India Prasad Krishna, Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal, India Mahesh B. Parappagoudar, Department of Mechanical Engineering, Chhatrapat...

In this paper, we propose an approach for solving hierarchical multi-objective optimization problems (MOPs). In realistic MOPs, two main challenges have to be considered: (i) the complexity of the search space and (ii) the non-monotonicity of the objective-space. Here, we introduce a hierarchical problem description (chromosomes) to deal with the complexity of the search space. Since Evolutiona...

Abstract Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and conflicting objectives optimized. Therefore, challage addressing CMOPs is how better balance objectives. To remedy this issue, paper proposes a novel dual-population based constrained evolutionary algorithm solve CMOPs, two popu...

Community detection is a crucial research direction in the analysis of complex networks and has been shown to be an NP-hard problem (a that at least as hard hardest problems nondeterministic polynomial time). Multi-objective evolutionary algorithms (MOEAs) have demonstrated promising performance community detection. Given distinct crossover operators are suitable for various stages algorithm ev...

Experienced users often have useful knowledge and intuition in solving real-world optimization problems. User can be formulated as inter-variable relationships to assist an algorithm finding good solutions faster. Such interactions also automatically learned from high-performing discovered at intermediate iterations run – a process called innovization. These relations, if vetted by the users, e...