Near Feasibility Driven Adaptive Penalty Functions Embedded MOEA/D

New concave penalty functions for improving the Feasibility Pump

Abstract Mixed-Integer optimization represents a powerful tool for modeling many optimization problems arising from real-world applications. The Feasibility pump is a heuristic for finding feasible solutions to mixed integer linear problems. In this work, we propose a new feasibility pump approach using concave nondifferentiable penalty functions for measuring solution integrality. We present c...

Penalty Functions

Restricted dissimilarity functions and penalty functions

In this work we introduce the definition of restricted dissimilarity functions and we link it with some other notions, such as metrics. In particular, we also show how restricted dissimilarity functions can be used to build penalty functions.

Solving DisCSPs with Penalty Driven Search

We introduce the Distributed, Penalty-driven Local search algorithm (DisPeL) for solving Distributed Constraint Satisfaction Problems. DisPeL is a novel distributed iterative improvement algorithm which escapes local optima by the use of both temporary and incremental penalties and a tabu-like no-good store. We justify the use of these features and provide empirical results which demonstrate th...

A Study of Adaptive Penalty Functions for Constrained Genetic Algorithm- Based Optimization

Constrained optimization via the Genetic Algorithm (GA) is often a challenging endeavor, as the GA is most directly suited to unconstrained optimization. Traditionally, external penalty functions have been used to convert a constrained optimization problem into an unconstrained problem for GA-based optimization. This approach requires the somewhat arbitrary selection of penalty draw-down coeffi...