In this work we present an overview of the most prominent population-based algorithms and
the methodologies used to extend them to multiple objective problems. Although not exact in …

Deep learning approaches have shown superior advantages than shallow techniques in the
field of electrical load forecasting; however, their applications in existing studies encounter …

Many real world engineering optimization problems are multi-modal and associated with
constrains. The multi-modal problems involve presence of local optima and thus …

The seasonality and randomness of wind present a significant challenge to the operation of
modern power systems with high penetration of wind generation. An effective short-term …

This paper presents an efficient multi-objective improved teaching–learning based
optimization (MO-ITLBO) algorithm for solving multi-objective optimization problems. The …

Multi-objective optimization problems (MOPs) with interval parameters are ubiquitous in real-
world applications. Existing evolutionary optimization methods, however, aim to obtain a set …

This work presents a high-fidelity surrogate model for generating a multi-objective genetic
algorithm to allow the search for the optimal geometry of the inlet channel and the basin of a …

This paper addresses the multi-objective portfolio selection model with fuzzy random returns
for investors by studying three criteria: return, risk and liquidity. In addition, securities …

Multi-objective particle swarm optimization (MOPSO), a population-based stochastic
optimization algorithm, has been successfully used to solve many multi-objective …

This paper presents an algorithm, named the uniform design multiobjective differential
algorithm based on decomposition (UMODE/D), for optimizing multiobjective problems. The …