Solving real-world multi-objective optimization problems using Multi-Objective Optimization Algorithms becomes difficult when the number of objectives is high since types algorithms generally used to solve these are based on concept non-dominance, which ceases work as grows. This problem known curse dimensionality. Simultaneously, existence many objectives, a characteristic practical problems, ...

Increasing of net energy storage (Q net) and discharge time of phase change material (t PCM), simultaneously, are important purpose in the design of solar systems. In the present paper, Multi-Objective (MO) based on hybrid of Particle Swarm Optimization (PSO) and multiple crossover and mutation operator is used for Pareto based optimization of solar systems. The conflicting objectives are Q net...

Most real world combinatorial optimization problems are difficult to solve with multiple objectives which have to be optimized simultaneously. Over the last few years, researches have been proposed several ant colony optimization algorithms to solve multiple objectives. The aim of this paper is to review the recently proposed multi-objective ant colony optimization (MOACO) algorithms and compar...

In this paper, experimentally derived correlations of heat transfer and pressure drop are used in a Pareto based Multi-Objective Optimization (MOO) approach to find the best possible combinations of heat transfer and pressure drop of TiO2-water nanofluid flow in tubes fitted with multiple twisted tape inserts in different arrangement. In this study there are four independent design variables: t...

We begin this paper by first comparing the theory of present-day portfolio selection, which is a theory for standard investors (whose utility functions take on only the single argument of portfolio return), with a developing theory for non-standard investors (whose utility functions are allowed to take on additional arguments). Examples of additional arguments are dividends, liquidity, social r...

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the applic...

Markov automata combine non-determinism, probabilistic branching, and exponentially distributed delays. This compositional variant of continuous-time Markov decision processes is used in reliability engineering , performance evaluation and stochastic scheduling. Their verification so far focused on single objectives such as (timed) reachability, and expected costs. In practice, often the object...

We propose a class of functions, called multiple objective satisficing (MOS) criteria, for evaluating the level of compliance of a set of objectives in meeting their targets collectively under uncertainty. The MOS criteria include the targets’ achievement probability (success probability criterion) as a special case and also extend to situations when the probability distribution is not fully ch...

Almost every real world problem involves simultaneous optimization of several incommensurable and often competing objectives which constitutes a multi-objective optimization problem. In multi-objective optimization problems the optimal solution is not unique as in single-objective optimization problems. This paper is concerned with large-scale structural optimization of skeletal structures such...