Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Falcón-Cardona, Jesús Guillermo; Gómez, Raquel Hernández; Coello, Carlos A. Coello; Tapia, Ma. Guadalupe Castillo

doi:10.1016/j.swevo.2021.100960

Cited by 30 publications

(8 citation statements)

References 108 publications

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“…Moreover, in the experiments, the computational time of solutions provided by the evolutionary algorithm increases with the number of smartphones and the total amount of battery changes involved in the preparation scenario, reaching nearly fifty seconds for the most complex tested scenario. Therefore, an extension we are planning for this research is improving the execution time of the algorithm by parallelizing the exploration of the solution space [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm

Yannibelli

Hirsch

Toloza

et al. 2023

Sensors

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Dew computing aims to minimize the dependency on remote clouds by exploiting nearby nodes for solving non-trivial computational tasks, e.g., AI inferences. Nowadays, smartphones are good candidates for computing nodes; hence, smartphone clusters have been proposed to accomplish this task and load balancing is frequently a subject of research. Using the same real—i.e., in vivo—testbeds to evaluate different load balancing strategies based on energy utilization is challenging and time consuming. In principle, test repetition requires a platform to control battery charging periods between repetitions. Our Motrol hard-soft device has such a capability; however, it lacks a mechanism to assure and reduce the time in which all smartphone batteries reach the level required by the next test. We propose an evolutionary algorithm to execute smartphone battery (dis)charging plans to minimize test preparation time. Charging plans proposed by the algorithm include charging at different speeds, which is achieved by charging at maximum speed while exercising energy hungry components (the CPU and screen). To evaluate the algorithm, we use various charging/discharging battery traces of real smartphones and we compare the time-taken for our method to collectively prepare a set of smartphones versus that of individually (dis)charging all smartphones at maximum speed.

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Section: Discussionmentioning

confidence: 99%

Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm

Yannibelli

Hirsch

Toloza

et al. 2023

Sensors

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“…Comparing two Pareto fronts is not straightforward as in the case of single-objective problems. In the literature, generational distance (GD), inverted generational distance (IGD), inverted generational distance plus (IGD+), and hypervolume (HV) [45], [46] have been used to compare Pareto solutions. From these, only IGD+ and HV are Pareto-compliant indicators, and thus used for a detailed comparison of FLEX-NSGA-II's performance with other algorithms.…”

Section: ) Performance Indicatorsmentioning

confidence: 99%

Local Fitness Landscape Exploration Based Genetic Algorithms

et al. 2023

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Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, "Fitness Landscape Exploration based Genetic Algorithm" (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEXbased algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.

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“…In such a model of m ∈ N slaves, the simultaneous evaluation of m individuals is possible, which leads to a significant reduction in the total evaluation time of the population. The parallel software implementation will be more meaningful in large-scale optimization problems [33][34][35][36][37][38][39][40].…”

Section: Parallel Master-slave Modelmentioning

confidence: 99%

“…Over the past decades, there has been a growing interest in the parallelization of metaheuristics algorithms [33][34][35][36][37][38][39][40]. Such advanced mechanisms for computation accelerating and enhancement greatly contribute to the success of metaheuristics for solving hard and large-scale optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…Several platforms and models, mainly the CPU-and GPU-based parallelization strategies, have been described and discussed. Another comprehensive survey on the parallel implementation of metaheuristics but within a multi-objective evolutionary framework is presented in [38]. An up-to-date review of methods and key contributions to such a research field are described.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

Jarray

Al‐Dhaifallah

Rezk

2022

Sensors

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The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.

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Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Cited by 30 publications

References 108 publications

Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm

Speeding up Smartphone-Based Dew Computing: In Vivo Experiments Setup Via an Evolutionary Algorithm

Local Fitness Landscape Exploration Based Genetic Algorithms

Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

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