Abstract Dynamic Multi-objective Optimization is a challenging research topic since the
objective functions, constraints, and problem parameters may change over time. Although …

Dynamic constrained multi-objective optimization problems (DCMOPs) involve objective
functions and constraints that vary over time, requiring optimization algorithms to track the …

Abstract Simulation-Based Multi-Objective Optimization (SBMOO) methods are being
increasingly used in conceptual architectural design. They mostly focus on the solving …

Residential consumers desire to minimize electricity bills while maximizing comfort by
appropriate appliance scheduling. The conflicting nature of the objectives facilitates a multi …

Recently, several researchers within the evolutionary and swarm computing community
have been interested in solving dynamic multi-objective problems where the objective …

Prediction methods are widely used to solve dynamic multi-objective optimization problems
(DMOPs). The key to the success of prediction methods lies in the accurate tracking of the …

Abstract Characterization of dynamism is an essential phase for some of the dynamic multi-
objective evolutionary algorithms (DMOEAs) in order to improve their performance. Although …

Most of the current research on dynamic multi-objective optimization problems (DMOPs)
assumes that environmental changes can be detectable. However, undetectable changes …

Smart home systems were introduced to autonomously control appliances, lights and other
services based on the current state inside homes. Smart home systems are designed for a …

Improvements in lighting and other indoor environmental conditions have gained
considerable attention in different areas, including health and economics. Controlling the …