In this paper, an angle based evolutionary algorithm with infeasibility information is proposed for constrained many-objective optimization, where different kinds of infeasible solutions are utilized in environmental selection and mating selection. To be specific, an angle-based constrained dominance relation...
While constrained, multiobjective optimization is generally very difficult, there is a special case in which such problems can be solved with a simple, elegant branch-and-bound algorithm. This special case is when the objective and constraint functions are Lipschitz continuous with known Lipschitz con...
Two-type weight adjustments in MOEA/D for highly constrained many-objective optimization 2021, Information Sciences Citation Excerpt : A CMaOEA is easy to be trapped in some locally feasible or infeasible areas and hard to approach the constrained PF. The key to overcoming the above two difficulti...
In this paper, an adaptive surrogate-assisted MOEA/D framework (ASA-MOEA/D) is proposed for solving computationally expensive constrained multi-objective optimization problems, in which three specific search strategies are adaptively implemented based on the optimization states of subproblems to achieve ta...
On top of the already complex constrained multi-objective optimization problem characteristics, there is also a need to reduce the computational and licensing cost involved. There are many engineering examples which require expensive function evaluations [35], special hardware, or software licences [37]...
Constrained optimization, also known as constraint optimization, is the process of optimizing an objective function with respect to a set of decision variables while imposing constraints on those variables. In this tutorial, we’ll provide a brief introduction to constrained optimization, explore some ...
Our method called Objective Exchange Genetic Algorithm for Design optimization (OEGADO) is intended for solving real-world application problems that have many consmints and very small feasible regions. OEGADO runs several GAs concurrently with each GA optimizing one objective and exchanging information ...
In this paper, we consider multi-objective optimization problems involving not necessarily convex constraints and componentwise generalized-convex (e.g., semi-strictly quasi-convex, quasi-convex, or explicitly quasi-convex) vector-valued objective functions that are acting between a real linear topologic...
Constrained optimization is a useful tool in designing or evaluating the health interventions comprising a package of care to offer in community health programs, by ensuring the resources are used in the most effective way to achieve a defined objective. Given the resources available for a maternal...
Many of the methods used in Optimization Toolbox™ solvers are based on trust regions, a simple yet powerful concept in optimization. To understand the trust-region approach to optimization, consider the unconstrained minimization problem, minimize f(x), where the function takes vector arguments an...