Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Evolutionary Computation at the Edge of Feasibility
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Brief paper: Robust PID controller tuning based on the constrained particle swarm optimization
Automatica (Journal of IFAC)
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Dynamic evolutionary algorithm with variable relocation
IEEE Transactions on Evolutionary Computation
Constraint handling in multiobjective evolutionary optimization
IEEE Transactions on Evolutionary Computation
Multi-objective optimization using self-adaptive differential evolution algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximation, and applications
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Constrained multi-objective optimization using steady state genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
About selecting the personal best in multi-objective particle swarm optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
IEEE Transactions on Evolutionary Computation
Cultural-Based Multiobjective Particle Swarm Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-Objective Optimization Based on Brain Storm Optimization Algorithm
International Journal of Swarm Intelligence Research
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Real-world optimization problems are often dynamic, multiple objective in nature with various constraints and uncertainties. This work proposes solving such problems by systematic segmentation via heuristic information accumulated through Cultural Algorithms. The problem is tackled by maintaining 1 feasible and infeasible best solutions and their fitness and constraint violations in the Situational Space, 2 objective space bounds for the search in the Normative Space, 3 objective space crowding information in the Topographic Space, and 4 function sensitivity and relocation offsets to reuse available information on optima upon change of environments in the Historical Space of a cultural framework. The information is used to vary the flight parameters of the Particle Swarm Optimization, to generate newer individuals and to better track dynamic and multiple optima with constraints. The proposed algorithm is validated on three numerical optimization problems. As a practical application case study that is computationally intensive and complex, parameter tuning of a PID Proportional-Integral-Derivative controller for plants with transfer functions that vary with time and imposed with robust optimization criteria has been used to demonstrate the effectiveness and efficiency of the proposed design.