Heuristic Optimization
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Revisiting Implicit and Explicit Averaging for Noisy Optimization
(20231001)Explicit and implicit averaging are two wellknown strategies for noisy optimization. Both strategies can counteract the disruptive effect of noise; however, a critical question remains: which one is more efficient? This ... 
DiscretizationBased Feature Selection as a Bilevel Optimization Problem
(20230801)Discretizationbased feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization ... 
Challenging test problems for multi and manyobjective optimization
(20230801)In spite of the extensive studies that have been conducted regarding the construction of multiobjective test problems, researchers have mainly focused their interests on designing complicated search spaces, disregarding, ... 
Time consistent expected meanvariance in multistage stochastic quadratic optimization: a model and a matheuristic
(2019)In this paper, we present a multistage time consistent Expected Conditional Risk Measure for minimizing a linear combination of the expected mean and the expected variance, socalled Expected MeanVariance. The model is ... 
The Natural Bias of Artificial Instances
(2023)Many exact and metaheuristic algorithms presented in the literature are tested by comparing their performance in different sets of instances. However, it is known that when these sets of instances are generated randomly, ... 
A revisited branchandcut algorithm for largescale orienteering problems
(20240216)The orienteering problem is a route optimization problem which consists of finding a simple cycle that maximizes the total collected profit subject to a maximum distance limitation. In the last few decades, the occurrence ... 
VSDMOEA: A DominanceBased Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management
(20220601)Most stateoftheart Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to ... 
On the utilization of pairpotential energy functions in multiobjective optimization
(20230601)In evolutionary multiobjective optimization (EMO), the pairpotential energy functions (PPFs) have been used to construct diversitypreserving mechanisms to improve Pareto front approximations. Despite PPFs have shown ... 
An ACObased Hyperheuristic for Sequencing Manyobjective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences
(20230201)Manyobjective optimization is an area of interest common to researchers, professionals, and practitioners because of its realworld implications. Preference incorporation into MultiObjective Evolutionary Algorithms (MOEAs) ... 
An integer programming model for obtaining cyclic quasidifference matrices
(20230101)Orthogonal arrays are of great importance in mathematical sciences. This paper analyses a certain practical advantage of quasidifference matrices over difference matrices to obtain orthogonal arrays with given parameters. ... 
Uncertaintywise software antipatterns detection: A possibilistic evolutionary machine learning approach
(20221101)Context: Code smells (a.k.a. antipatterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap: Existing works did not take into account the issue of ... 
On the Construction of ParetoCompliant Combined Indicators
(20220812)The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect ... 
A convergence and diversity guided leader selection strategy for manyobjective particle swarm optimization
(20221001)Recently, particle swarm optimizer (PSO) is extended to solve manyobjective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection ... 
An Ensemble SurrogateBased Framework for Expensive Multiobjective Evolutionary Optimization
(20220801)Surrogateassisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, ... 
Multiple source transfer learning for dynamic multiobjective optimization
(20220801)Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs ... 
A selforganizing weighted optimization based framework for largescale multiobjective optimization
(20220701)The solving of largescale multiobjective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a selforganizing weighted optimization ... 
Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters
(20220701)Multiobjective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Manyobjective Optimization Problems (MaOPs). Decompositionbased strategies, such as MOEA/D, divide an MaOP ... 
Static and Dynamic Multimodal Optimization by Improved Covariance Matrix SelfAdaptation Evolution Strategy with Repelling Subpopulations
(20220601)The covariance matrix selfadaptation evolution strategy with repelling subpopulations (RSCMSAES) is one of the most successful multimodal optimization (MMO) methods currently available. However, some of its components ... 
A dynamic multiobjective evolutionary algorithm based on polynomial regression and adaptive clustering
(20220601)In this paper, a dynamic multiobjective evolutionary algorithm is proposed based on polynomial regression and adaptive clustering, called DMOEAPRAC. As the Paretooptimal solutions and fronts of dynamic multiobjective ... 
AdaSwarm: Augmenting GradientBased Optimizers in Deep Learning with Swarm Intelligence
(20220401)This paper introduces AdaSwarm, a novel gradientfree optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially ...