abstract in this paper, a fuzzy pid with new structure is proposed to solve the load frequency control in interconnected power systems. in this study, a new structure and effective of the fuzzy pid-type load frequency control (lfc) is proposed to solve the load frequency control in interconnected power systems. the main objective is to eliminate the deviations in the frequency of different area...

The Pareto front reflects the optimal trade-offs between conflicting objectives and can be used to quantify the effect of different beam configurations on plan robustness and dose-volume histogram parameters. Therefore, our aim was to develop and implement a method to automatically approach the Pareto front in robust intensity-modulated proton therapy (IMPT) planning. Additionally, clinically r...

We address the problem of multi-objective constraint optimization problems (MO-COPs). Solving an MO-COP traditionally consists in computing the set of all Pareto solutions (i.e. the Pareto front). But this Pareto front is exponentially large in the general case. So this causes two main problems. First is the time complexity concerns. Second is a lack of decisiveness. In this paper, we present t...

To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms part...

In real world problems, one is often faced with the problem of multiple, possibly competing, goals, which should be optimized simultaneously. These competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. If none of the objectives have preference over the other, none of these trade-off solutions can be said to be better than any other solution in the set....

In this research report, the author proposes two new evolutionary approaches to Multiobjective Optimization Problems (MOPs)— Dynamic Particle Swarm Optimization (DPSMO) and Dynamic Particle Swarm Evolutionary Algorithm (DPSEA). In DPSMO, instead of using genetic operators (e.g., crossover and mutation), the information sharing technique in Particle Swarm Optimization is applied to inform the en...

We propose a new algorithm of computation using particle swarm in order to solve multi-objective problems more quickly and e ectively. This approach, called accelerated multi-objective particle swarm, is partially based on our previous work [4] and incorporates a vector function as objective function and it uses matrix computation to develop the Pareto front. Unlike all these studies which use ...

A novel approach is presented in this article for obtaining inverse mapping of thermodynamically Pareto-optimized ideal turbojet engines using group method of data handling (GMDH)-type neural networks and evolutionary algorithms (EAs). EAs are used in two different aspects. Firstly, multi-objective EAs (non–dominated sorting genetic algorithm-II) with a new diversity preserving mechanism are us...

Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all candidates for which no other candidate scores better under both objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into a single objective, but is more costly to compute. We define a general Pareto produ...

In this paper we propose the multi-objective contextual bandit problem with similarity information. This problem extends the classical contextual bandit problem with similarity information by introducing multiple and possibly conflicting objectives. Since the best arm in each objective can be different given the context, learning the best arm based on a single objective can jeopardize the rewar...