Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps ...
Stephanus Daniel Handoko, Chee Keong Kwoh, Yew-Soo...
Abstract. We explore a new general-purpose heuristic for nding highquality solutions to hard optimization problems. The method, called extremal optimization, is inspired by self-or...
Stefan Boettcher, Allon G. Percus, Michelangelo Gr...
Abstract. Stochastic optimization is a leading approach to model optimization problems in which there is uncertainty in the input data, whether from measurement noise or an inabili...
Many-objective optimization refers to optimization problems with a number of objectives considerably larger than two or three. In this paper, a study on the performance of the Fast...
In this paper, we explore the feasibility and performance optimization problems for real-time systems that must remain functional during an operation/mission with a fixed, initial...
Relay power allocation has been shown to provide substantial performance gain in wireless relay networks when perfect global channel state information (CSI) is available. In this p...
Tony Q. S. Quek, Moe Z. Win, Hyundong Shin, Marco ...
Abstract. We present EvA2, a comprehensive metaheuristic optimization framework with emphasis on Evolutionary Algorithms. It presents a modular structure of interfaces and abstract...
The Quadratic Assignment Problem (QAP) is a well-known NP-hard combinatorial optimization problem that is at the core of many real-world optimization problems. We prove that QAP c...
In many optimization problems, one seeks to allocate a limited set of resources to a set of individuals with demands. Thus, such allocations can naturally be viewed as vectors, wi...
In this paper, we present a stigmergy-based algorithm for solving optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The perfor...