25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

This paper presents a novel, optimization algorithm called Equilibrium Optimizer (EO), inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts as a search agent. The search agents randomly update their concentration with respect to best-so-far solutions, namely equilibrium candidates, to finally reach to the equilibrium state (optimal result). A well-defined “generation rate” term is proved to invigorate EO’s ability in exploration, exploitation, and local minima avoidance. The proposed algorithm is benchmarked with 58 unimodal, multimodal, and composition functions and three engineering application problems. Results of EO are compared to three categories of existing optimization methods, including: (i) the most well-known meta-heuristics, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO); (ii) recently developed algorithms, including Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Salp Swarm Algorithm (SSA); and (iii) high performance optimizers, including CMA-ES, SHADE, and LSHADE-SPACMA. Using average rank of Friedman test, for all 58 mathematical functions EO is able to outperform PSO, GWO, GA, GSA, SSA, and CMA-ES by 60%, 69%, 94%, 96%, 77%, and 64%, respectively, while it is outperformed by SHADE and LSHADE-SPACMA by 24% and 27%, respectively. The Bonferroni–Dunnand Holm’s tests for all functions showed that EO is significantly a better algorithm than PSO, GWO, GA, GSA, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-SPACMA. The source code of EO is publicly availabe at https://github.com/afshinfaramarzi/Equilibrium-Optimizer , http://built-envi.com/portfolio/equilibrium-optimizer/ and http://www.alimirjalili.com/SourceCodes/EOcode.zip .

Equilibrium optimizer: A novel optimization algorithm

Surrogate-assisted, or meta-model based evolutionary computation uses efficient computational models, often known as surrogates or meta-models, for approximating the fitness function in evolutionary algorithms. Research on surrogate-assisted evolutionary computation began over a decade ago and has received considerably increasing interest in recent years. Very interestingly, surrogate-assisted evolutionary computation has found successful applications not only in solving computationally expensive single- or multi-objective optimization problems, but also in addressing dynamic optimization problems, constrained optimization problems and multi-modal optimization problems. This paper provides a concise overview of the history and recent developments in surrogate-assisted evolutionary computation and suggests a few future trends in this research area.

http://www.soft-computing.de/SECPublished.pdf

Surrogate-assisted evolutionary computation: Recent advances and future challenges

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Linear And Nonlinear Programming

This paper presents a nature-inspired metaheuristic called Marine Predators Algorithm (MPA) and its application in engineering. The main inspiration of MPA is the widespread foraging strategy namely Levy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems. This paper evaluates the MPA's performance on twenty-nine test functions, test suite of CEC-BC-2017, randomly generated landscape, three engineering benchmarks, and two real-world engineering design problems in the areas of ventilation and building energy performance. MPA is compared with three classes of existing optimization methods, including (1) GA and PSO as the most well-studied metaheuristics, (2) GSA, CS and SSA as almost recently developed algorithms and (3) CMA-ES, SHADE and LSHADE-cnEpSin as high performance optimizers and winners of IEEE CEC competition. Among all methods, MPA gained the second rank and demonstrated very competitive results compared to LSHADE-cnEpSin as the best performing method and one of the winners of CEC 2017 competition. The statistical post hoc analysis revealed that MPA can be nominated as a high-performance optimizer and is a significantly superior algorithm than GA, PSO, GSA, CS, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-cnEpSin. The source code is publicly available at: https://github.com/afshinfaramarzi/Marine-Predators-Algorithm, http://built-envi.com/portfolio/marine-predators-algorithm/, https://www.mathworks.com/matlabcentral/fileexchange/74578-marine-predators-algorithm-mpa, and http://www.alimirjalili.com/MPA.html.

Marine Predators Algorithm: A nature-inspired metaheuristic

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.

A hybrid genetic algorithm for constrained optimization problems in mechanical engineering

Performance profiles are an analytical tool for the visualization and interpretation of the results of benchmark experiments. In this paper we discuss their explanatory power, and argue that they should be more widely used by the evolutionary computation community. We also introduce some novel performance measures which can be extracted from the performance profiles. In order to illustrate their potential, we apply the referred profiles to the analysis of the results of the CEC 2006 constrained optimization competition. While some of the results are corroborated, some new facts are pointed out and additional conclusions are drawn.

Using performance profiles to analyze the results of the 2006 CEC constrained optimization competition

A new hybrid AIS-GA for constrained optimization problems in mechanical engineering

Ant colony approaches for multiobjective structural optimization problems with a cardinality constraint

Artificial Immune Systems (AISs) are computational methods inspired by the biological immune system and thus classified as a nature-inspired meta-heuristic along with genetic algorithms, ant colony optimization, particle swarm optimization, and others. This chapter is focused on the application of AISs to solve optimization problems. Optimization is a mathematical principle largely applied to design and operational problems in all types of engineering, as well as a tool for formulating and solving inverse problems such as parameter identification in scientific and engineering situations. This chapter provides a survey of the applications of AISs in optimization. The main contributions are studied and contrasted with respect to the different concepts of the biological immune system, such as clonal selection, hypermutation, immune network, affinity, etc. that have inspired such techniques. The main types of optimization problems are considered, namely, (i) unconstrained problems, (ii) constrained problems, (iii) multimodal problems where several optima are to be obtained in a single run, and, finally (iv) multi-objective problems, where an approximation to the Pareto set is to be obtained as the result of a single run of the algorithm. Although AISs are good for solving optimization problems, useful features from other techniques have been combined with a “pure” AIS in order to generate hybrid AIS methods with improved performance.

Heder S. Bernardino

Papers

A hybrid genetic algorithm for constrained optimization problems in mechanical engineering

Using performance profiles to analyze the results of the 2006 CEC constrained optimization competition

A new hybrid AIS-GA for constrained optimization problems in mechanical engineering

Ant colony approaches for multiobjective structural optimization problems with a cardinality constraint

Artificial Immune Systems for Optimization