The advent of numerical computational approaches permits evolutionary algorithms (EAs) to solve complex, real-world engineering problems. The additional modification or hybridization of such EAs in academic research a...
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The advent of numerical computational approaches permits evolutionary algorithms (EAs) to solve complex, real-world engineering problems. The additional modification or hybridization of such EAs in academic research and application demonstrates improved performance for domain-specific challenges. However, developing a new algorithm or comparison and selection of existing EAs for challenges in the field of optimization is relatively unexplored. The performance of different well-established algorithms is, therefore, investigated in this work. The selection of algorithms using nonparametric tests encompasses different categories to include- Genetic Algorithm, Particle Swarm Optimization, Harmony Search Algorithm, Cuckoo Search Algorithm, Bat Algorithm, Firefly algorithm, Differential Evolution, and Artificial Bee Colony. These algorithms are applied to solve test functions, including unconstrained, constrained, industry specific problems, CEC 2011 real world optimization problems and selected CEC 2013 benchmark test functions. The three distinct performance metrics, namely, efficiency, reliability, and quality of solution derived using the quantitative attributes are provided to evaluate the performance of the employed EAs. The categorical assignment of performance attributes helps to compare different algorithms for a specific optimization problem while the performance metrics are useful to provide the common platform for new or hybrid EA development.
For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control pa...
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ISBN:
(纸本)9781728145334
For general video game playing agents, the biggest challenge is adapting to the wide variety of situations they encounter and responding appropriately. Some success was recently achieved by modifying search-control parameters in agents on-line, during one play-through of a game. We propose adapting such methods for Rolling Horizon evolutionary algorithms, which have shown high performance in many different environments, and test the effect of on-line adaptation on the agent's win rate. On-line tuned agents are able to achieve results comparable to the state of the art, including first win rates in hard problems, while employing a more general and highly adaptive approach. We additionally include further insight into the algorithm itself, given by statistics gathered during the tuning process and highlight key parameter choices.
The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is to maximize the total profit of the ...
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ISBN:
(纸本)9781450371285
The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is to maximize the total profit of the selected items under the condition that the weight of the selected items only exceeds the given weight bound with a small probability of alpha. In this paper, we consider problem-specific single-objective and multi-objective approaches for the problem. We examine the use of heavy-tail mutations and introduce a problem-specific crossover operator to deal with the chance-constrained knapsack problem. Empirical results for single-objective evolutionary algorithms show the effectiveness of our operators compared to the use of classical operators. Moreover, we introduce a new effective multi-objective model for the chance-constrained knapsack problem. We use this model in combination with the problem-specific crossover operator in multi-objective evolutionary algorithms to solve the problem. Our experimental results show that this leads to significant performance improvements when using the approach in evolutionary multi-objective algorithms such as GSEMO and NSGA-II.
This article explores the application of evolutionary algorithms and agent-oriented programming to solve the problem of searching and monitoring objectives through a fleet of unmanned aerial vehicles. The subproblem o...
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ISBN:
(纸本)9783030410056;9783030410049
This article explores the application of evolutionary algorithms and agent-oriented programming to solve the problem of searching and monitoring objectives through a fleet of unmanned aerial vehicles. The subproblem of static off-line planning is studied to find initial flight plans for each vehicle in the fleet, using evolutionary algorithms to achieve compromise values between the size of the explored area, the proximity of the vehicles, and the monitoring of points of interest defined in the area. The results obtained in the experimental analysis on representative instances of the surveillance problem indicate that the proposed techniques are capable of computing effective flight plans.
We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) - each implementing a different search paradigm...
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ISBN:
(纸本)9783030581114;9783030581121
We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) - each implementing a different search paradigm - by comparing run-time convergence behaviour over a set of 1200 problem instances. The new benchmarks are created by fusing previously proposed single-objective interpolated continuous optimisation problems (ICOPs) via a common set of Pareto non-dominated seeds. They thus inherit the ICOP property of having tunable fitness landscape features. The benchmarks are of intrinsic interest as they derive from interpolation methods and so can approximate general problem instances. This property is revealed to be of particular importance as our extensive set of numerical experiments indicates that choices pertaining to (i) the weighting of the inverse distance interpolation function and (ii) the problem dimension can be used to construct problems that are challenging to all tested multi-objective search paradigms. This in turn means that the new multi-objective ICOPs problems (MO-ICOPs) can be used to construct well-balanced benchmark sets that discriminate well between the run-time convergence behaviour of different solvers.
Excel solver is a powerful tool for optimization of linear and nonlinear problems. With this unique tool, the user can achieve an optimal value for the desired objective function in Excel cell. This solver acts on a g...
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Excel solver is a powerful tool for optimization of linear and nonlinear problems. With this unique tool, the user can achieve an optimal value for the desired objective function in Excel cell. This solver acts on a group of cells that are directly or indirectly associated with the function;thus, the user-defined values will be optimized. In the present work, 13 existing species in an electrolyte solution have been considered to predict the activity coefficient of inorganic ions in the electrolyte solution, which includes H2O, CO2(aq), H+, Na+, Ba2+, Ca2+, Sr2+, Mg2+, OH-, Cl-, SO4, CO3, HCO3. In this study, to predict the activity coefficient of species in the system, Extended UNIQUAC activity coefficient model was considered and its parameters optimized using Excel solver tool based on GRG algorithm. Total error for optimization of adjustable parameters of Extended UNIQUAC model for 13 desired ions at three temperatures 298.15 K, 323.15 K and 373.15 K with the Excel solver tool was 0.0087. The results of GRG algorithm were favorable than those of ICA, PSO and ABC algorithms. The results of this optimization are intended to predict mineral deposition. The number of adjustable variables (model parameters for optimization) is over 200, and the number of target functions is 39.
This paper attempts to employ evolutionary Algorithm(EA) techniques to evolve variants of a computer virus(Timid) that successfully evades popular antivirus scanners. Generating authentic variants of a specific malwar...
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This paper attempts to employ evolutionary Algorithm(EA) techniques to evolve variants of a computer virus(Timid) that successfully evades popular antivirus scanners. Generating authentic variants of a specific malware results in a valid database of malware variants, which is sought by anti-malware scanners, so as to identify the variants before they are released by malware developers. This preliminary investigation applies EAs to mutate the Timid virus with a simple code evasion strategy, i.e., insertion and deletion(if available) of a specific assembly code instruction directly into the virus source code. Starting with a database of over 60 popular antivirus scanners, this EA based approach for malware variant generation successfully evolves Timid variants that evade more than 97% of the antivirus scanners. The results from these preliminary investigations demonstrate the potential for EA based malware generation and also opens up avenues for further analysis.
Solving single objective constrained real-parameter optimization problems via population-based algorithms has attracted much attention. In this paper, a new self-tuning meta-heuristic approach called Fuzzy Controlled ...
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ISBN:
(纸本)9781728169293
Solving single objective constrained real-parameter optimization problems via population-based algorithms has attracted much attention. In this paper, a new self-tuning meta-heuristic approach called Fuzzy Controlled Cooperative Heterogeneous Algorithm (FCHA), which was proposed for constrained optimization, is introduced. The developed approach combines competition and cooperation between biology-inspired and evolutionary algorithms, regulated by fuzzy controller. It should be noted, that the epsilon-constrained method is utilized to handle the constraints for the solved optimization problems. The performance of the proposed FCHA algorithm is evaluated on 57 real-world constrained problems submitted for CEC 2020 special session. Its workability and usefulness are demonstrated;also ways of algorithm improvement are discussed.
One of the possible ways to increase the end-to-end power transfer efficiency in a radiative Wireless Power Transfer (WPT) system is by transmitting multi-tone signals optimized according to the receiver rectenna'...
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ISBN:
(纸本)9781728166872
One of the possible ways to increase the end-to-end power transfer efficiency in a radiative Wireless Power Transfer (WPT) system is by transmitting multi-tone signals optimized according to the receiver rectenna's nonlinear behavior and the Channel State Information (CSI). This optimization problem is a non-convex problem that has been tackled in the past with Sequential Convex Programming (SCP) algorithms. Since SCP algorithms do not guarantee to track the globally optimal solutions, there is interest in applying some other optimization methods to this problem. Here we apply various evolutionary algorithms (EAs) with different characteristics. The performance of the designed waveforms is evaluated in Matlab, using a simplified Single Input Single Output (SISO) system model. EAs are successfully applied to waveform design for WPT and seem to track the optimal solutions in the tested cases. Moreover, the effectiveness of the SCP-QCLP method is verified.
This paper proposes a new parent selection strategy for reducing the effect of evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms. Asynchronous parallel evolutionary algorithms have ...
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ISBN:
(纸本)9781728125473
This paper proposes a new parent selection strategy for reducing the effect of evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms. Asynchronous parallel evolutionary algorithms have a problem to biased toward the search region with short evaluation time. The proposed method selects parent solutions that take into account the search progress of each solution. This paper conducts an experiment to investigate the effectiveness of the proposed method. The experiment uses NSGA-HI. a multi-objective evolutionary algorithm, and compares the synchronous NSGA-III, the asynchronous NSGA-III, and the asynchronous NSGA-III with the proposed selection method. The experimental result reveals that the proposed method can reduce the effect of the evaluation time bias while reducing the computing time of the parallel NSGA-III.
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