Combining ideas inspired by ant colonies and flocks of birds may hold the key to unlocking more effective artificial ...

Paper aims To do a comprehensive review of the exact and heuristic methods, software/programming languages, constraints, and types of analysis (technical and fundamental) used to solve the portfolio ...

Abstract: Particle swarm optimization, as an evolutionary computing technique, has succeeded in many continuous problems, but research on discrete problems especially combinatorial optimization ...

Abstract: This paper proposes an improved version of the random drift particle swarm optimization algorithm for solving the economic dispatch problem. The improvement is achieved through adding a ...

In response to the shortcomings of the Salp Swarm Algorithm (SSA) such as low convergence accuracy and slow convergence speed, a Multi-Strategy-Driven Salp Swarm Algorithm (MSD-SSA) was proposed.

The uncertainty of renewable energy and demand response brings many challenges to the microgrid energy management. Driven by the recent advances and applications of deep reinforcement learning a ...

On my NPR-San Francisco show, listeners would call in with a problem and, in a few minutes, I had to come up with an approach that we both felt good about. Over the years, I refined the approach and ...

The current release of this book can be found at here. This book was desigend originally for the undergraduete course ISE 3434 - "Deterministic Operations Research II" taught at Virginia Tech. I will ...