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Advances in Sine Cosine Algorithm: A comprehensive survey

ARTIFICIAL INTELLIGENCE REVIEW 2021年第4期54卷 2567-2608页

作者： Abualigah, Laith Diabat, Ali Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan New York Univ Abu Dhabi Div Engn Saadiyat Isl 129188 U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

The Sine Cosine Algorithm (SCA) is a population-based optimization algorithm introduced by Mirjalili in 2016, motivated by the trigonometric sine and cosine functions. After providing an overview of the SCA algorithm, we survey a number of SCA variants and applications that have appeared in the literature. We then present the results of a series of computational experiments to validate the performance of the SCA against similar algorithms.

关键词： Sine Cosine Algorithm Meta-heuristic optimization algorithms Optimization problems population-based algorithms

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Multiobjective Interacting Particle Algorithm for Global Optimization

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INFORMS JOURNAL ON COMPUTING 2014年第3期26卷 500-513页

作者： Mete, Huseyin Onur Zabinsky, Zelda B. Univ Washington Seattle WA 98195 USA

We develop a population-based algorithm for the optimization of multiple, nonconvex, nondifferentiable, and possibly discontinuous objective functions. The algorithm employs Markov kernels, Hit-and-Run, and Pattern Hit-and-Run for exploration of the solution space and Pareto ordering rules for the selection of the population and to update the approximate Pareto optimal list. Our multiobjective interacting particle algorithm asymptotically converges to the stationary distribution associated with the Pareto ordering rules. We present numerical benchmark results on test problems.

关键词： multiobjective optimization random search algorithms global optimization simulated annealing Markov chain Monte Carlo sampling Pareto optimality efficient frontier population-based algorithms Pattern Hit-and-Run

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Attraction-Repulsion Optimization Algorithm for Global Optimization Problems

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 84卷

作者： Cymerys, Karol Oszust, Mariusz Rzeszow Univ Technol Dept Comp & Control Engn Wincentego Pola 2 PL-35959 Rzeszow Poland

In this study, a novel meta-heuristic search (MHS) algorithm for constrained global optimization problems is proposed. Since many algorithms aim to achieve well-balanced exploitation-exploration stages with often unsatisfactory results, in the approach introduced in this paper, Attraction-Repulsion Optimization Algorithm (AROA), the balance associated with attraction-repulsion phenomena that occur in nature is mimicked. AROA introduces a search strategy in which a candidate solution is moved in the search space depending on the quality of solutions in its neighborhood, as well as the best candidate. The candidates are managed by local search operators based on modified Brownian motion, trigonometric functions, randomly selected solutions, and a form of memory. Consequently, AROA exhibits a satisfactory exploitation-exploration balance exhibited by highly competitive performance. The introduced algorithm is experimentally compared with the state-ofthe-art meta-heuristics on the CEC 2014, 2017, and 2020 test suites. The obtained results reveal the advantages of AROA over related algorithms and its suitability in solving complex real-world problems.

关键词： Global optimization population-based algorithms Meta-heuristics Attraction-repulsion operator Attraction-repulsion optimization algorithm (AROA)

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Dynamic optimization facilitated by the memory tree

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SOFT COMPUTING 2015年第3期19卷 547-566页

作者： Zhu, Tao Luo, Wenjian Yue, Lihua Univ Sci & Technol China Sch Comp Sci & Technol Anhui Prov Key Lab Software Engn Comp & Commun Hefei 230027 Anhui Peoples R China

Memorizing the past information for later environments is an effective and widely employed approach to optimize dynamic problems. Although the existing explicit memories for dynamic optimization differ widely in the literature, all of them organize memory entries in a linear list. This naive structure leads to problems, such as heavy computational overhead and small memory capacity, and thus restricts the performance of the memories. In this paper, the binary space partition tree is adopted to organize the memory entries, and then a memory tree is constructed. The memory tree partitions the search space into regions. In order to make use of the memory tree, a neighbor shift strategy is proposed. When a new individual is generated in a region that has never been visited since the last change, the new individual is shifted to the neighboring memory individual of that region, if it is less fit than the memory individual. The proposed approach can be easily combined with many population-based algorithms for dynamic optimization in the real space. As examples, the proposed approach was combined with a basic particle swarm optimizer and two state-of-the-art dynamic optimizers. The experimental results showed that it significantly enhanced the performance of the three optimizers on various test problems. The proposed approach demonstrates the importance of memory structure in memory approaches.

关键词： Dynamic optimization problem population-based algorithms Memory Memory structure Binary space partition tree

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Multi-objective simulation optimization for complex urban mass rapid transit systems

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ANNALS OF OPERATIONS RESEARCH 2021年第1-2期305卷 449-486页

作者： Schmaranzer, David Braune, Roland Doerner, Karl F. Univ Vienna Dept Business Decis & Analyt Oskar Morgenstern Pl 1 A-1090 Vienna Austria Univ Vienna Christian Doppler Lab Efficient Intermodal Transp Oskar Morgenstern Pl 1 A-1090 Vienna Austria Univ Vienna Data Sci Uni Vienna Oskar Morgenstern Pl 1 A-1090 Vienna Austria

In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We propose a two-phase algorithm that combines the single-objective covariance matrix adaptation evolution strategy with a problem-specific multi-directional local search. With a computational study, we compare our proposed method against both a multi-objective covariance matrix adaptation evolution strategy and a non-dominated sorting genetic algorithm. The integrated discrete event simulation model has several stochastic elements. Fluctuating demand (i.e., creation of passengers) is driven by hourly origin-destination-matrices based on mobile phone and infrared count data. We also consider the passenger distribution along waiting platforms and within vehicles. Our two-phase optimization scheme outperforms the comparative approaches, in terms of both spread and the accuracy of the resulting Pareto front approximation.

关键词： Multi-objective optimization Simulation optimization Headway optimization Transit network frequencies setting problem population-based algorithms Multi-directional local search Public transportation

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Three approximation algorithms for solving the generalized segregated storage problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2004年第1期156卷 54-72页

作者： Barbucha, D Gdynia Maritime Acad Fac Business Adm Dept Informat Syst PL-81225 Gdynia Poland

The paper presents three approximation algorithms for solving the generalized segregated storage problem (GSSP). GSSP involves determining an optimal distribution of goods among a set of storage compartments with the segregation (physical separation) restrictions. GSSP is a new generalization of well-known segregated storage problem. The paper gives problem formulation and proposes three approximation algorithms for solving it: a specialized construction heuristic and two population-based algorithms: an evolutionary algorithm and a population learning algorithm. The algorithms are evaluated in computational experiments. The analysis of variance method was used for statistical analysis of obtained results. (C) 2003 Elsevier B.V. All rights reserved.

关键词： combinatorial optimization segregated storage problems heuristics population-based algorithms

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Optimal Mutation Rates for the (1+λ) EA on OneMax 16th

Optimal Mutation Rates for the (1+λ) EA on OneMax

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16th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Buzdalov, Maxim Doerr, Carola ITMO Univ St Petersburg Russia Sorbonne Univ LIP6 CNRS Paris France

ISBN: (纸本)9783030581145;9783030581152

The OneMax problem, alternatively known as the Hamming distance problem, is often referred to as the "drosophila of evolutionary computation (EC)", because of its high relevance in theoretical and empirical analyses of EC approaches. It is therefore surprising that even for the simplest of all mutation-based algorithms, Randomized Local Search and the (1 + 1) EA, the optimal mutation rates were determined only very recently, in a GECCO 2019 poster. In this work, we extend the analysis of optimal mutation rates to two variants of the (1 + lambda) EA and to the (1 + lambda) RLS. To do this, we use dynamic programming and, for the (1 + lambda) EA, numeric optimization, both requiring Theta(n(3)) time for problem dimension n. With this in hand, we compute for all population sizes lambda is an element of {2(i) vertical bar 0 <= i <= 18} and for problem dimension n is an element of {1000, 2000, 5000} which mutation rates minimize the expected running time and which ones maximize the expected progress. Our results do not only provide a lower bound against which we can measure common evolutionary approaches, but we also obtain insight into the structure of these optimal parameter choices. For example, we show that, for large population sizes, the best number of bits to flip is not monotone in the distance to the optimum. We also observe that the expected remaining running times are not necessarily unimodal for the (1 + lambda) EA(0 -> 1) with shifted mutation.

关键词： Parameter control Optimal mutation rates population-based algorithms OneMax

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Step-Size Individualization: a case study for the Fish School Search Family

Step-Size Individualization: a case study for the Fish Schoo...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Amorim Neto, Hugo de Andrade Pereira de Lacerda, Marcelo Gomes de Lima Neto, Fernando Buarque Univ Pernambuco Comp Engn EComp Recife PE Brazil

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

This study proposes a new strategy to improve the performance of the algorithms of the Fish School Search (FSS) family via the individualization of the step-size of each fish. We propose to be calculated in two different manners: using individual weight or using individual fitness, depending on the chosen variation of the proposed technique. Our methods were tested on the original FSS, on the Weight based Fish School Search (wFSS) and on the Multi Objective Fish School Search (MOFSS) algorithms. The benchmark functions of the Congress on Evolutionary Computation, The Genetic and Evolutionary Computation Conference (CEC'2020, CEC'2013, and GECCO'2016) and the DTLZ test suite were used to assess the experimental results, which yielded that all variants of the FSS algorithm tested have been improved in the majority of the scenarios.

关键词： Metaheuristic population-based algorithms self-adapting parameters Fish School Search

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An Information-Theoretic Approach for Clonal Selection algorithms

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9th International Conference on Artificial Immune Systems (ICARIS 2010)

作者： Cutello, Vincenzo Nicosia, Giuseppe Pavone, Mario Stracquadanio, Giovanni Univ Catania Dept Math & Comp Sci I-95125 Catania Italy

ISBN: (纸本)9783642145469

In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions;the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Renyi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values.

关键词： Clonal selection algorithms population-based algorithms information theory relative entropy global numerical optimization

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Memetic Differential Evolution Using Coordinate Descent

Memetic Differential Evolution Using Coordinate Descent

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Bidgoli, Azam Asilian Rahnamayan, Shahryar Ontario Tech Univ Dept Elect Comp & Software Engn Nat Inspired Computat Intelligence NICI Lab Oshawa ON Canada

ISBN: (纸本)9781728183923

Differential Evolution (DE) is one of the well-established population-based optimization algorithms which has received a lot of attention regarding its potential to solve complex optimization problems. However, DE is capable to explore a huge search space in its early run phase, called exploration phase, its weakness in exploitation avoids local refinement of the promising shrunk region. Therefore, employing a local search can be an efficient strategy to improve the search performance of DE via accelerating of fine tuning phase. This paper purposes an effective Memetic DE algorithm using a well-known single-solution-based optimization method, i.e., Coordinate Descent (CD) algorithm. Local coordinate search is applied on the promising region resulted by top ranked individuals selected from the final population of DE. The proposed method updates the value of each coordinate iteratively by evaluating the sampled points from the local region to improve the resulted candidate solution. Since coordinate search algorithm shrinks the region rapidly, it requires a very small portion of the computational budget to find the optimal coordinates' value. In order to evaluate the proposed Memetic DE, several experiment series are conducted on functions of CEC-2017 benchmark for different number of dimensions (i.e., D=30, 50, and 100). Results clearly indicate that the utilized local coordinate search improves the quality of resulted solution by DE significantly using a very low computational budget, i.e., 20xD.

关键词： Differential Evolution (DE) Coordinate Descent Local Search population-based algorithms Metaheuristics Memetic Algorithm

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