Problems with two or more conflicting objectives have been handled as needing a multi-objective approach in recent years. The solution for these types of problems is normally to satisfy the simultaneously order find tradeoffs between different criteria. Optimization power systems an important example how tackle since performance indicators and operate close their constraints due continuous incr...

Abstract Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive problems. However, relatively little work been dedicated addressing constrained multi-objective discrete problems although there are many such the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed this paper for kind of problem. Specifically, random forest m...

Striking a balance between objective optimization and constraint satisfaction is essential for solving constrained multi-objective problems (CMOPs). Nevertheless, most existing evolutionary algorithms face significant challenges on CMOPs with intricate infeasible regions. To tackle these challenges, this paper proposes an adaptive two-population algorithm, named ATEA, which dynamically exploits...

This paper addresses the problem of controlling mutation strength in multi-objective evolutionary algorithms and its implications for the convergence to the Pareto set. Adaptive parameter control is one major issue in the field of evolutionary computation, and several methods have been proposed and applied successfully for single objective optimization problems. In this study we examine whether...

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to ...

In the firefighter problem the spread of fire is modelled on an undirected graph. The goal is to find such an assignment of firefighters to the nodes of the graph that they save as large part of the graph as possible. In this paper a multi-objective version of the firefighter problem is proposed and solved using an evolutionary algorithm. Two different autoadaptation mechanisms are used for gen...

This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combin...

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