A multi-objective framework for distributed energy resources planning and storage management
The use of energy storage systems (ESS) and distributed generators (DGs) to improve
reliability is one of the solutions that has received much attention from researchers today. In …
reliability is one of the solutions that has received much attention from researchers today. In …
Micro multi-strategy multi-objective artificial bee colony algorithm for microgrid energy optimization
Multi-objective evolutionary algorithm (MOEA) has become a common and effective method
to solve real-world multi-objective optimization problems. However, in some practical …
to solve real-world multi-objective optimization problems. However, in some practical …
[HTML][HTML] A late-mover genetic algorithm for resource-constrained project-scheduling problems
Abstract The Resource-Constrained Project Scheduling Problem (RCPSP) plays a critical
role in various management applications. Despite its importance, research efforts are still …
role in various management applications. Despite its importance, research efforts are still …
μMOSM: A hybrid multi-objective micro evolutionary algorithm
In multi-objective optimization problems (MOPs), several mutually conflicting objectives are
optimized simultaneously. In such scenarios, there is not a unique solution to the problem; …
optimized simultaneously. In such scenarios, there is not a unique solution to the problem; …
Multi-task optimization in reliability redundancy allocation problem: A multifactorial evolutionary-based approach
Evolutionary multi-task optimization attempts to solve multiple optimization problems
simultaneously by modeling the solution structures of two or more problems within a single …
simultaneously by modeling the solution structures of two or more problems within a single …
Micro Many-Objective Evolutionary Algorithm With Knowledge Transfer
Computational effectiveness and limited resources in evolutionary algorithms are
interdependently handled during the working of low-power microprocessors for real-world …
interdependently handled during the working of low-power microprocessors for real-world …
Constrained multi-objective optimization with deep reinforcement learning assisted operator selection
Solving constrained multi-objective optimization problems with evolutionary algorithms has
attracted considerable attention. Various constrained multi-objective optimization …
attracted considerable attention. Various constrained multi-objective optimization …
Comparative analysis of selection hyper-heuristics for real-world multi-objective optimization problems
As exact algorithms are unfeasible to solve real optimization problems, due to their
computational complexity, meta-heuristics are usually used to solve them. However …
computational complexity, meta-heuristics are usually used to solve them. However …
Social Sustainability and Resilience in Supply Chains of Latin America on COVID-19 Times: Classification Using Evolutionary Fuzzy Knowledge
The number of research papers interested in studying the social dimension of supply chain
sustainability and resilience is increasing in the literature. However, the social dimension is …
sustainability and resilience is increasing in the literature. However, the social dimension is …
Micro Multiobjective Evolutionary Algorithm With Piecewise Strategy for Embedded-Processor-Based Industrial Optimization
In some industrial applications, it is required to do off-line multiobjective optimization in
embedded systems. Due to their limited computing and memory capability, embedded …
embedded systems. Due to their limited computing and memory capability, embedded …