The article develops hierarchical population game models of co-evolutionary algorithms for solving the problem multi-criteria optimization under uncertainty. principles vector minimax and risk are used as basic optimality concept equilibrium a with right first move is defined. necessary conditions formulated which solution discrete approximation set optimal solutions to

Four multi-objective evolutionary optimization algorithms are discussed with respect to their efficiency in portfolio optimization problems. The assessment of the advantages and disadvantages of the considered algorithms is based on experimental study where two and three criteria portfolio optimization problems were used as tests. The performance of considered algorithms are presented and compa...

Distributed constraint optimization (DCOP) has emerged as a key technique for multiagent coordination. Unfortunately, while previous work in DCOP focuses on optimizing a single team objective, domains often require satisfying additional criteria. This paper provides a novel multi-criteria DCOP algorithm, based on two key ideas: (i) transforming multi-criteria problems via virtual variables to h...

Distributed constraint optimization (DCOP) has emerged as a key technique for multiagent coordination. Unfortunately, while previous work in DCOP focuses on optimizing a single team objective, domains often require satisfying additional criteria. This paper provides a novel multi-criteria DCOP algorithm, based on two key ideas: (i) transforming multi-criteria problems via virtual variables to h...

The multiple criteria nature of most real world problems has boosted research on multi-objective algorithms that can tackle such problems effectively, with the smallest possible computational burden. Particle Swarm Optimization has attracted the interest of researchers due to its simplicity, effectiveness and efficiency in solving numerous single-objective optimization problems. Up-to-date, the...

Many real-world search and optimization problems involve inequality and/or equality constraints and are thus posed as constrained optimization problems. In trying to solve constrained optimization problems using classical optimization methods, this paper presents a Multi-Objective Bees Algorithm (MOBA) for solving the multi-objective optimal of mechanical engineering problems design. In the pre...

In this work, a multi-objective hybrid optimizer is presented. The optimizer uses several multi-objective evolutionary optimization algorithms and orchestrates the application of these algorithms to multi-objective optimization problems, using an automatic internal switching algorithm. The switching algorithm is designed to favor those search algorithms that quickly improve the Pareto approxima...