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This paper proposes a new constraint handling method named Angle-based Constrained Dominance Principle (ACDP). Unlike the original Constrained Dominance Principle (CDP), this approach adopts the angle information of the objective functions to enhance the population's diversity in the infeasible region. To be more specific, given two infeasible solutions, if the angle of the solutions is greater than...
This paper presents a random-based dynamic grouping strategy (RDG) for cooperative coevolution to deal with large scale multi-objective optimization problems (MOPs) by decomposing the whole dimension into several groups of variables with an equal size. First, a decomposer pool containing different group sizes is designed. Then, a group size is dynamically selected with probability in the evolution...
The Multi-objective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES) is an evolutionary algorithm for continuous vector optimization. It is invariant against rotations and translations of the search space and empirical evaluations have shown that it is very competitive with other popular multi-objective evolutionary algorithms, like NSGA-II. However, MO-CMA-ES requires a certain “warm-up...
Selective Cationic Antibacterial Peptides (SCAPs) are becoming a potential alternative to known antibiotics to deal with multi-drug resistant pathogens. Conventional techniques for the discovery and design of SCAPs can be time consuming and expensive, therefore the use of computers to aid such discovery or design is becoming attractive since they can help to reduce the number of sequences to be evaluated...
Indicator-based evolutionary algorithm (IBEA1) is a fast and effective approach for solving multiobjective optimization problems (MOPs). In the classical IBEA1, the parameter κ is predefined to amplify or shrink the indicator differences on pairwise solutions. However, the value of κ in IBEA1 needs to be carefully calibrated based on the selected indicator (e.g., hypervolume or additive e-indicator)...
The decomposition method in multiobjective evolutionary algorithms (MOEA/D) is an effective approach to evolve solutions along predefined weight vectors for solving multiobjective optimization problems (MOPs). However, obtaining evenly distributed weight vectors for different types of MOPs is a challenge problem especially when the true Pareto fronts (PFs) are unknown before a MOEA/D starts. In this...
Locating the source of diffusion is a challenging problem in complex networks and has great practical significance for restraining rumors propagation and controlling epidemics spreading. An efficient locating method should have a higher locating accuracy with the minimum required information. Although existing locating methods based on observers consider the time delays of edges, they compute the...
An opposition-based learning competitive particle swarm optimizer (OBL-CPSO) is proposed to address the problem of premature convergence in PSO. Two learning mechanisms have been employed in OBL-CPSO, which are competitive learning from competitive swarm optimizer (CSO) and opposition-based learning. In each iteration of OBL-CPSO, the competitive learning works among three randomly selected particles...
The problem of network disintegration has broad applications and recently has received growing attention, such as network confrontation and disintegration of harmful networks. This paper presents an optimized disintegration strategy model for complex networks and introduces the GA optimization method into the network disintegration problem to identify the optimal disintegration strategy, which is...
Scale-free networks are commonly used to model real-world physical and virtual systems, such as transportation networks, power grids, telecommunication networks etc. These networks are often vulnerable to malicious attacks, and making them resilient (robust) to such attacks is of significant research interest. There are various types of malicious attacks, but a particularly destructive one is the...
The community structure detection of complex networks has become a hot topic in the past several years. In this paper, a new discrete framework of population-based incremental learning for complex networks problem is proposed. Based on the proposed discrete framework, a novel multi-objective population-based incremental learning algorithm is proposed to solve community structure detection problem...
This paper presents a differential evolutionary clustering approach to solve the optimization of the dimension weights in subspace, which is referred to as Soft Subspace Clustering Using Differential Evolutionary Algorithm (DESSC). The classical clustering methods can handle the low-dimensional rather than the high-dimensional data due to the curse of dimensionality. In addition, many subspace clustering...
This study is a novel contribution to the field of optimization in home health care services, both model and solution approach. We address an integration of interrelated optimization problems: rostering, assignment, routing, and scheduling in multi-period workforce planning under uncertainty in nurse availability. Our model explicitly handles the constraints related to workload balancing and multi-period...
Interval programming problems are ubiquitous in real-world situations. There exist a variety of theories and methods for handling them; the existing methods, however, have adopted various dominance criteria to distinguish solutions, and these criteria are always subjective. Different dominance criteria will produce different optimal solution(s), and subjective criteria make users, especially for those...
Adaptation to changes in real scenarios is not a “cost-free” operation. However, in general, this is not considered in most of the studies done in dynamic optimization problems. Our focus here is to analyse what happens when a relocation cost is added in the dynamic maximal covering location problem, that is, when the adaptation to changes entails some cost. Comparing two models with and without “cost-free”...
The quantum particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO) algorithm aimed at solving dynamic optimization problems. Some particles in the QPSO algorithm are selected as “quantum” particles and the positions of these particles are sampled, using some probability distribution, within a radius (i.e., a hypersphere) around the global best...
Multi-Objective Optimization (MOO) problems might be subject to many modeling or manufacturing uncertainties that affect the performance of the solutions obtained by a multi-objective optimizer. The decision maker must perform an extra step of sensitivity analysis in which each solution should be verified for its robustness, but this post optimization procedure makes the optimization process expensive...
Many real-world optimization problems encounter the presence of uncertainties. Dynamic optimization is a class of problems whose fitness functions vary through time. For these problems, evolutionary algorithm is expected to adapt to the changing environments immediately and find the best solution accurately. Besides, most of the environmental changes may not be too drastic in real-world applications,...
Solving sparse optimization problems via regularization frameworks is the dominant methodology for reconstructing sparse signals in the area of compressive sensing. In recent a few years, the use of multiobjective evolutionary algorithms (MOEAs) for sparse optimization has also attracted some research interests. Under the multiobjective framework, the loss term (error) and the regularization term...
Coevolutionary games cast players that may change their strategies as well as their networks of interaction. In this paper a framework is introduced for describing coevolutionary game dynamics by landscape models. It is shown that coevolutionary games invoke dynamic landscapes. Numerical experiments are shown for prisoner's dilemma (PD) and snow drift (SD) games that both use either birth-death (BD)...
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