Offspring generation plays an important role in evolutionary multiobjective optimization.
However, generating promising candidate solutions effectively in high-dimensional spaces …

Crossover is a crucial operation for generating promising offspring solutions in evolutionary
multi-objective optimization. Among various crossover operators, the simulated binary …

The convergence and diversity enhancement of multiobjective evolutionary algorithms
(MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active …

Evolutionary multi-objective optimization aims at obtaining a set of Pareto-optimal solutions
among the multiple conflicting objectives. However, the ability of multi-objective evolutionary …

Most existing dominance relations give higher priority to convergence than diversity and
cannot offer reasonable selection pressure according to the evolution status. This easily …

Abstract The Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D)
overcomes the limitation of evolutionary algorithm based on a Pareto dominant relationship …

Many-objective optimization problems (MaOPs) have attracted wide attention. However,
most solving methods prioritize the convergence or take the convergence and diversity into …

Multiobjective optimization (MOO) is a popular procedure for waterflooding optimization
under geological uncertainty that maximizes the expectation of net present value (NPV) over …

In practice, the multi-objective optimization problem (MOP) is typically challenging in two
aspects. On the one hand, its Pareto front has imbalanced search difficulties; on the other …

Hydraulic fracturing is the most widely used technology for the commercial exploitation of
shale-gas reservoirs, yet still faces high cost and development risk caused by many …