Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

In their original versions, nature-inspired search algorithms such as evolutionary algorithms
and those based on swarm intelligence, lack a mechanism to deal with the constraints of a …

This paper presents the interior search algorithm (ISA) as a novel method for solving
optimization tasks. The proposed ISA is inspired by interior design and decoration. The …

In this study, we use a new metaheuristic optimization algorithm, called bat algorithm (BA), to
solve constraint optimization tasks. BA is verified using several classical benchmark …

This paper proposes a constraint handling technique for multiobjective evolutionary
algorithms based on an adaptive penalty function and a distance measure. These two …

Many real world engineering optimization problems are multi-modal and associated with
constrains. The multi-modal problems involve presence of local optima and thus …

This paper proposes a self adaptive penalty function for solving constrained optimization
problems using genetic algorithms. In the proposed method, a new fitness value, called …

This paper proposes an adaptive penalty function for solving constrained optimization
problems using genetic algorithms. The proposed method aims to exploit infeasible …

Convolutional neural networks (CNNs) have attracted increasing attention in recent years
because of their powerful abilities to extract and represent spatial/temporal information …

A parameter‐less adaptive penalty scheme for genetic algorithms applied to constrained
optimization problems is proposed. Using feedback from the evolutionary process the …