Solving Complex Multi-objective Problems Research Articles

Leader recommend operators selection strategy for a multiobjective evolutionary algorithm based on decomposition

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) shows strong performance in solving complex multi-objective problems (MOPs), wherein evolutionary operators have great influence on the search ability. Appropriate operators can greatly improve the performance of MOEA/D. However, one omnipotent operator usually cannot handle all different MOPs very well. According to the characteristics of the problems, the operators that adapt to the problems are also different. This paper proposes an adaptive operator selection strategy called Leader Recommend Operator Selection (LROS). We construct an operator pool consisting of Simulated Binary Crossover (SBX) and three operators of Differential Evolution (DE). Meanwhile, we divide the entire evolutionary process into several stages, and each stage is divided into Part 1 and Part 2. In Part 1 of each stage, we use a subset of individuals to test the performance of each operator in the operator pool and choose the best performance. In Part 2, we adopt the best performing operator selected in Part 1 to generate offspring, meanwhile we verify the actual quality of these offspring to decide in the next stage whether to continue using this operator to generate offspring or to choose a more suitable operator. Experimental results demonstrate the effectiveness of LROS.

Multi-objective meta-heuristic optimization in intelligent control: A survey on the controller tuning problem

Multi-objective optimization has been adopted in many engineering problems where a set of requirements must be met to generate successful applications. Among them, there are the tuning problems from control engineering, which are focused on the correct setting of the controller parameters to properly govern complex dynamic systems to satisfy desired behaviors such as high accuracy, efficient energy consumption, low cost, among others. These requirements are stated in a multi-objective optimization problem to find the most suitable controller parameters. Nevertheless, these parameters are tough to find because of the conflicting control performance requirements (i.e., a requirement cannot be met without harming the others). Hence, the use of techniques from computational intelligence and soft computing is necessary to solve multi-objective problems and handle the trade-offs among control performance objectives. Meta-heuristics have shown to obtain outstanding results when solving complex multi-objective problems at a reasonable computational cost. In this survey, the literature related to the use of multi-objective meta-heuristics in intelligent control focused on the controller tuning problem is reviewed and discussed.

Multi-objective particle swarm optimization based on cooperative hybrid strategy

A multi-objective particle swarm optimization based on cooperative hybrid strategy (CHSPSO) is presented in this paper to solve complex multi-objective problems. Most algorithms usually contain only one strategy, which makes them unable to trade off the convergence and diversity when solving the complex multi-objective problems. The proposed cooperative hybrid strategy can effectively guarantee the convergence and the diversity of the algorithm. The multi-population strategy and the dynamic clustering strategy are employed to improve the convergence and the diversity. At the same time, the life strategy and lottery probability selection strategy are used to further ensure the diversity of the population. A series of test functions are used to verify the effectiveness of CHSPSO. The performance of the proposed algorithm is compared with other evolutionary algorithms. The results show that CHSPSO can obtain a better convergence and diversity for the complex multi-objective problems.

A multi-objective mixed-discrete particle swarm optimization with multi-domain diversity preservation

Among population-based optimization algorithms guided by meta-heuristics, Particle Swarm Optimization (PSO) has gained significant popularity in the past two decades, particularly due to its ease of implementation and fast convergence capabilities. This paper seeks to translate the beneficial features of PSO from solving typical continuous single-objective problems to solving multi-objective mixed-discrete problems, which is relatively a new ground for PSO application. The original Mixed-Discrete PSO (MDPSO) algorithm, which included an exclusive diversity preservation technique to significantly mitigate premature particle clustering, has been shown to be a powerful single-objective solver for highly constrained MINLP problems. This papers makes fundamental advancements to MDPSO, enabling it to solve complex multi-objective problems with mixed-discrete design variables. Specifically, in the velocity update equation for any particle, the explorative term is modified to point towards a stochastically selected non-dominated solution at that iteration ? thereby adopting the concept of multi-leader swarms. The fractional domain in the diversity preservation technique, which was previously defined in terms of the best global particle, is now formulated as a function of the extreme members in the set of intermediate Pareto optimal solutions. With this advancement, diversity preservation not only mitigates premature particle stagnation, but also promotes more uniform coverage of the Pareto frontier. The multi-objective MDPSO algorithm is tested using a set of benchmark problems and a wind farm layout optimization problem. To illustrate the competitive benefits of the new MO-MDPSO algorithm, the results are compared with those given by other popular multi-objective solvers such as NSGA-II and SPEA.

Difference Selection Strategy for Solving Complex Multi-Objective Problems

Difference Selection Strategy for Solving Complex Multi-Objective Problems

MULTIOBJECTIVE OPTIMIZATION OF DYNAMIC PROCESSES BY EVOLUTIONARY METHODS

The real-world optimisation of dynamic processes, such as batch processes, space applications and robotic problems, is usually a matter of several objectives and constraints. In many cases it is difficult to deal with such problems with conventional methods. Evolutionary methods provide an interesting alternative, with less programming and computational efforts. This paper presents four Evolutionary methods for solving complex multiobjective problems applied to an illustrative example: the optimisation and control of the industrial beer fermentation. The first method is based on aggregating functions, and the others adopt a Pareto set approach.