A new multi-swarm multi-objective optimization method for structural design

A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems

“…Katsigiannis et al [21] developed a multiobjective optimization model to generate a PF to minimize the total cost of energy and total greenhouse gas emissions of an HRES during its lifetime by using a nondominated sorting GA (NSGA). In Trivedi [22], a multiobjective GA (MOGA) was applied to solve a nonlinear MOP for scheduling a wind/diesel system, which aims to minimize fuel cost as well as SO 2 and NO x emission. Rodolfo et al [23] applied a Strength Pareto Evolutionary Algorithm to determine the optimal size and optimal power management strategy parameters for an HRES with the aim of minimizing total cost, unmet load, and fuel emission simultaneously.…”

Microprocessors optimal power dissipation using combined threshold hopping and voltage scaling

“…Basically for Intel Core i7 processors, temperatures are classified into temperatures between (32-72)°C to specify idle, normal and maximum temperatures respectively. This indicate that, for a specific load, in order to maintain optimal power and temperatures, there are a huge number of V th -V dd combination sets and the search space of the PSO becomes an acceptable very broad and the computation time increased dramatically [17].…”

Application of Multi-objective Charged System Search Algorithm for Optimization Problems

“…After that, a wide variety of MOEAs have been suggested, such as Micro-Genetic Algorithm (Micro-GA) [19] , Non-dominated Sorting Genetic Algorithm (NSGA) [20] , New variant of NSGA or NSGA-II [21] , Strength Pareto Evolutionary Algorithm (SPEA) [22] , SPEA2 [23] , Pareto Archive Evolution Strategy (PAES) [24] , Pareto Differential Evolution Approach (PDEA) [25], MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition [26], NSGAII based on Differential Evolution (NSGAII-DE [27], A hybrid multi-objective particle swarm optimization and decision making procedure for optimal design of truss structures [28], The third version of Generalized Differential Evolution (GDE3) [29], Multi-Objective Differential 4 Evolution-the Ranking-based Mutation Operator (MODE-RMO) [30], Multi-Objective Particle Swarm Optimization (MOPSO) [31], Differential Evolution for Multi-Objective Optimization (DEMO) [32], A novel hybrid charge system search and particle swarm optimization method for multi-objective optimization [33], Multi-Objective Differential Evolution (MODE) [34], MultiObjective bees algorithms(Bees) [35] and Non-dominated Rank Genetic Algorithm (NRGA) [36]. Recent years, some other types of algorithms were also developed such as Multi-Objective Cuckoo Search (MOCS) [37], Multi-Objective Firefly Algorithm (MOFA) [38], A new multiswarm multi-objective optimization method for structural design [39], A swarm based memetic evolutionary algorithm for multi-objective optimization of large structures [40], Multi-Objective Flower Pollination Algorithms (MOFPA) [17] and Multi-objective Optimization Method Based on Sensitivity Analysis [14].…”