We study time-varying nonlinear optimizations with time-varying linear equality constraints and adapt Z-type neural-dynamics (ZTND) for solving such problems.
The obtained outcomes have been compared with two own developed methods, namely, particle swarm optimization (PSO) and particle swarm optimization with time varying acceleration coefficients (PSO-TVAC).
Optimization with time-dependent partial differential equations (PDEs) as constraints appears in many science and engineering applications.
In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals.
In this paper, Binary Particle Swarm Optimization with Time-Varying Acceleration Coefficients (BPSO-TVAC) is proposed to maximize profit subject to turbine position, turbine size, hub height, annual energy production, investment budget, land lease cost, operation and maintenance cost and levelized replacement cost constraints.
One-parametric NLP or MINLP optimizations with residence time as a variating parameter are performed to construct trajectories in the EAR.
However, computational costs are still a great constraint for PSO, as well as for all other PBMs, especially in optimization problems with time consuming objective functions.
With the goal of reducing cost, improving customer satisfaction and controlling the environmental pollution, a environmental routing optimization problem with time windows and multiple vehicle types is proposed by considering the concept of low-carbon logistics.
This paper proposes a binary particle swarm optimization (BPSO) with time-varying acceleration coefficients (TVAC) for solving optimal placement of wind turbines within a wind farm.
By adopting a well-defined accessibility measure, this paper studies a new discrete network design problem for metropolitan areas, in which some concepts, including the accessible flow, travel time budget function and principles of user equilibrium and system optimization with travel time budgets, are proposed.
The original optimization problem with time-dependent constraints is replaced by an equivalent problem with stationary constraints by means of Laplace transforms.
