With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous...

The traffic signal design problem is a typical multiobjective optimization problem. In this research, a simulation-based multiobjective evolutionary approach for traffic signal design problems is proposed. This approach has a robust advantage that the numerical simulators and the optimization solvers can be changed and used easily for other applications. In this research, it combined the NSGA-I...

In real life, there are a lot of Multi-objective Optimization Problem, which is shorted for MOP in the process of people working in production and economic and engineering activities. These problems are often very complex and nonlinear, and even conflicted with each other. When solving these problems, Multi-objective Optimization, shorted for MOO should be done on these issues. For example, for...

Building maintenance management involves decision-making under multiple objectives and uncertainty, in addition to budgetary constraints. This paper presents the development of a multiobjective and stochastic optimization system for maintenance management of roofing systems that integrates stochastic condition assessment and performance prediction models with a multiobjective optimization appro...

Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization a...

The traditional single objective mean variance optimization model fails to satisfy the investors with multiple investment objectives. So multi-objective portfolio optimization model is considered in this paper. Since this will help investors to achieve highest expected return among the different financial products of the capital market and to fulfill the expected return objectives simultaneousl...

Machine learning often requires the optimization of multiple, partially conflicting objectives. True multi-objective optimization (MOO) methods avoid the need to choose a weighting of the objectives a priori and provide insights about the tradeoffs between the objectives. We extend a state-of-the-art derivative-free Monte Carlo method for MOO, the MO-CMA-ES, to operate on an unbounded set of (n...