Advanced Optimization by Nature-Inspired Algorithms

Chapter 1: Overview of Optimization<br>Summary<br>This chapter briefly explains optimization and its basic concepts. Also, examples of the different types of engineering optimization problems are presented in this chapter.<br>1.1 Optimization&nbsp;&nbsp;&nbsp; <br>1.2 Examples of engineering optimization problems<br>1.3 Conclusion&nbsp;&nbsp;&nbsp; <br><br>Chapter 2: Introduction to Meta-heuristic and Evolutionary Algorithms<br>Summary<br>This chapter begins with a brief review of different independent-problem methods for searching the decision space, describes the components of meta-heuristic and evolutionary algorithms by relating them to engineering optimization problems. Other related topics such as coding meta-heuristic and evolutionary algorithms, dealing with constraints, objective functions, solution strategies, are reviewed. A general algorithm is presented that encompasses most of the steps of all known meta-heuristic and evolutionary algorithms. This generic presentation provides a standard reference with which to compare all the known meta-heuristic and evolutionary algorithms. The chapter closes with the performance evaluation of the meta-heuristic and evolutionary algorithms covered by the book.<br>2.1 Searching decision space for optima&nbsp;&nbsp;&nbsp; <br>2.2 Definition of terms related meta-heuristic and evolutionary algorithms&nbsp;&nbsp;&nbsp; <br>2.3 Foundation of meta-heuristic and evolutionary algorithms&nbsp;&nbsp;&nbsp; <br>2.4 Classification of meta-heuristic and evolutionary algorithms&nbsp;&nbsp;&nbsp; <br>2.5 Coding meta-heuristic and evolutionary algorithms in both discrete and continuous domains&nbsp;&nbsp;&nbsp; <br>2.6 Generating random values&nbsp;&nbsp;&nbsp; <br>2.7 Dealing with constraints&nbsp;&nbsp;&nbsp; <br>2.8 Fitness functions&nbsp;&nbsp;&nbsp; <br>2.9 Selection of decision variables, parameters&nbsp;&nbsp;&nbsp; <br>2.10 Generating new solutions&nbsp;&nbsp;&nbsp; <br>2.11 The best solution&nbsp;&nbsp;&nbsp; <br>2.12 Termination criteria<br>2.13 General algorithm<br>2.14 Performance evaluation of meta-heuristic and evolutionary algorithms<br>2.15 Conclusion<br><br>Chapter 3: Pattern Search (PS)<br>Summary<br>This chapter explains the pattern search (PS) algorithm, which is classified as a direct search method. The chapter starts with a brief literature review of the development of PS, important modification of the algorithm, and its applications to engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the different steps of the algorithm are described in detail. A pseudo code of the algorithm is presented that serves as an easy and sufficient guideline for coding the algorithm.<br>3.1 Introduction<br>3.2 Pattern search (PS) foundation<br>3.3 Generating initial solution<br>3.4 Generate trial solutions<br>3.5 Update mesh size<br>3.6 Termination criteria<br>3.7 User-defined parameters of the PS<br>3.8 Pseudo code of the PS<br>3.9 Conclusion<br>3.10 References<br><br>Chapter 4: The Genetic Algorithm (GA)<br>Summary<br>This chapter describes the genetic algorithm (GA), which is a well-known evolutionary algorithm. The chapter starts with a brief literature review of the GA’s development, followed by presentation of the modification that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the different steps of the algorithm are described in detail. A pseudo code of the algorithm is presented that serves as an easy and sufficient guideline for coding the algorithm.<br>4.1 Introduction<br>4.2 Mapping natural evolution into genetic algorithm (GA)<br>4.3 Creating the initial population<br>4.4 Selection of decision variables, parameters<br>4.4.1. Proportionate selection<br>4.4.2. Ranking selection<br>4.4.3. Tournament selection<br>4.5 Reproduction<br>4.6 Population diversity and selective pressure4.7 Termination criteria<br>4.8 User-defined parameters of the GA<br>4.9 Pseudo code of the GA<br>4.10 Conclusion<br>4.11 References<br><br>Chapter 5: Simulated Annealing (SA)<br>Summary<br>This explains the simulated annealing (SA) algorithm, which is inspired by the process of annealing in metal work. The chapter starts with a brief literature review of the SA development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>5.1 Introduction<br>5.2 Mapping physical annealing process into simulated annealing (SA) algorithm<br>5.3 Generating initial state<br>5.4 Generating a new state<br>5.5 Acceptance function<br>5.6 Temperature equilibrium<br>5.7 Temperature reduction<br>5.8 Termination criteria<br>5.9 User-defined parameters of the SA<br>5.10 Pseudo code of the SA<br>5.11 Conclusion<br>5.12 References<br><br>Chapter 6: The Tabu Search Algorithm (TSA)<br>Summary<br>This chapter explains the Tabu search algorithm (TSA) which is combinatorial in nature. The chapter starts with a brief literature review of the TSA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.6.1 Introduction<br>6.2 Tabu search foundation<br>6.3 Generating initial searching point<br>6.4 Neighbor points<br>6.5 Tabu list<br>6.6 Updating Tabu list<br>6.7 Attributive Memory<br>6.8 Aspiration criteria<br>6.9 Intensification and diversification strategies<br>6.10 Termination criteria6.11 User-defined parameters of the TS<br>6.12 Pseudo code of the TS<br>6.13 Conclusion<br>6.14 References<br><br>Chapter 7: Ant Colony Optimization (ACO)<br>Summary<br>This chapter explains ant colony optimization (ACO). The basic concepts of the ACO are derived from nature and are based on the forging behavior of ants. The chapter starts with a brief literature review of ACO’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.7.1 Introduction<br>7.2 Mapping ants’ behavior into ant colony optimization (ACO)<br>7.3 Creating the initial population<br>7.4 Allocating pheromone to decision space<br>7.5 Generation new solutions<br>7.6 Termination criteria<br>7.7 User-defined parameters of the ACO<br>7.8 Pseudo code of the ACO<br>7.9 Conclusion<br>7.10 References<br><br>Chapter 8: Particle Swarm Optimization (PSO)<br>Summary<br>This describes the particle swarm optimization (PSO) technique which is based on the swarm intelligence mechanism and behavior of swarms. The chapter starts with a brief literature review of the PSO’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>8.1 Introduction<br>8.2 Mapping social behavior into particle swarm optimization<br>8.3 Creating the initial population of particles<br>8.4 Personal and global best position<br>8.5 Velocities of particles<br>8.6 Update the particle’s position<br>8.7 Termination criteria<br>8.8 User-defined parameters of the PSO<br>8.9 Pseudo code of the PSO<br>8.10 Conclusion<br>8.11 References<br><br>Chapter 9: Differential Evolution (DE)<br>Summary<br>This chapter describes differential evolution (DE). The DE, which is basically a parallel direct search method that takes advantage of some features of evolutionary algorithms (EAs), is a simple yet powerful meta-heuristic method. The chapter starts with a brief literature review of DE’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>9.1 Introduction<br>9.2 Differential evolution (DE) foundation<br>9.3 Creating the initial population<br>9.4 Generating trial solutions<br>9.5 Greedy criteria<br>9.6 Termination criteria<br>9.7 User-defined parameters of the DE<br>9.8 Pseudo code of the DE<br>9.9 Conclusion<br>9.10 References<br><br>Chapter 10: Harmony Search (HS)<br>Summary<br>This chapter describes the harmony search (HS) which is a meta-heuristic algorithm for discrete optimization. The chapter starts with a brief literature review of HS’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>10.1 Introduction<br>10.2 Inspiration of harmony search (HS)<br>10.3 Initializing harmony memory<br>10.4 Improvising new harmony<br>10.5 Updating the harmony memory<br>10.6 Termination criteria<br>10.7 User-defined parameters of the HS<br>10.8 Pseudo code of the HS<br>10.9 Conclusion&lt;10.10 References<br><br>Chapter 11: The Shuffled Frog-Leaping Algorithm (SFLA)<br>Summary<br>This chapter explains the shuffled frog-leaping algorithm (SFLA). The SFLA is a swarm intelligence algorithm based on the memetic evolution of the social behavior of frogs. The chapter starts with a brief literature review of SFLA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>11.1 Introduction<br>11.2 Mapping memtic evolution of frogs into the SFLA<br>11.3 Creating the initial population<br>11.4 Classification of frogs into memeplexes<br>11.5 Frog leaping<br>11.6 Shuffling process<br>11.7 Termination criteria<br>11.8 User-defined parameters of the SFLA<br>11.9 Pseudo code of the SFLA<br>11.10 Conclusion<br>11.11 References<br><br>Chapter 12: Honey-Bee Mating Optimization (HBMO)<br>Summary<br>This chapter describes the honey-bee mating optimization (HBMO) algorithm which is based on the honey-bees’ social structure and mating in the bee hive. The chapter starts with a brief literature review of HBMO’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>12.1 Introduction<br>12.2 Mapping honey-bee colony structure into the HBMO algorithm<br>12.3 Creating the initial population<br>12.4 Queen&nbsp;&nbsp;&nbsp; <br>12.5 Drone selection<br>12.6 Brood production<br>12.7 Improving broods by workers<br>12.8 Termination criteria12.9 User-defined parameters of the HBMO<br>12.10 Pseudo code of the HBMO<br>12.11 Conclusion<br>12.12 References<br><br>Chapter 13: Invasive Weed Optimization (IWO)<br>Summary<br>This chapter describes the invasive weed optimization (IWO) algorithm which mimics the adaptive and evolutionary characteristics of weeds. The chapter starts with a brief literature review of IWO’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>13.1 Introduction<br>13.2 Mapping weeds’ biology into invasive weed optimization (IWO)<br>13.3 Creating the initial population<br>13.4 Reproduction<br>13.5 Spread of seeds<br>13.6 Eliminate weeds with low fitness<br>13.7 Termination criteria<br>13.8 User-defined parameters of the IWO<br>13.9 Pseudo code of the IWO&nbsp;&nbsp;&nbsp; <br>13.10 Conclusion<br>13.11 References<br><br>Chapter 14: Central Force Optimization (CFO)<br>Summary<br>This chapter describes the central force optimization (CFO) algorithm. The basic concepts of the CFO come from kinesiology in physics. The chapter starts with a brief literature review of CFO’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>14.1 Introduction<br>14.2 Mapping Newton’s gravitational low into the central force optimization (CFO)<br>14.3 Initializing the position of probes<br>14.4 Calculation of accelerations<br>14.5 Movement of Probes<br>14.6 Modification of deviated probes&nbsp;&nbsp;&nbsp; <br>14.7 Termination criteria<br>14.8 User-defined Parameters of the CFO<br>14.9 Pseudo code of the CFO&nbsp;&nbsp;&nbsp; <br>14.10 Conclusion<br>14.11 References<br><br>Chapter 15: Biogeography-Based Optimization (BBO)<br>Summary<br>This chapter describes the biogeography-based optimization (BBO) which is inspired by the science of biogeography. The chapter starts with a brief literature review of BBO’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>15.1 Introduction<br>15.2 Mapping biogeography concepts into biogeography-based optimization (BBO)<br>15.3 Creating the initial population<br>15.4 Migration process<br>15.5 Mutation<br>15.6 Termination criteria<br>15.7 User-define parameters of the BBO<br>15.8 Pseudo code of the BBO&nbsp;&nbsp;&nbsp; <br>15.9 Conclusion<br>15.10 References<br><br>Chapter 16: The Firefly Algorithm (FA)<br>Summary<br>This chapter describes the firefly algorithm (FA) which is inspired by the flashing light emitted by fireflies. The chapter starts with a brief literature review of the FA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>16.1 Introduction<br>16.2 Mapping behavior of fireflies into firefly algorithm (FA)<br>16.3 Creating the initial population<br>16.4 Attractiveness<br>16.5 Distance and Movement<br>16.6 Termination criteria<br>16.7 User defined parameters of the FA16.8 Pseudo code of the FA<br>16.9 Conclusion<br>16.10 References<br><br>Chapter 17: The Gravity Search Algorithm (GSA)<br>Summary<br>This chapter explains the gravity search algorithm (GSA). The GSA is an evolutionary optimization algorithm based on the law of gravity and mass interactions. The chapter starts with a brief literature review of the GSA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>17.1 Introduction<br>17.2 Mapping the law of gravity into gravity search algorithm (GSA)<br>17.3 Creating the initial population<br>17.4 Evaluation of particle's mass<br>17.5 Update velocities and positions<br>17.6 Update Newton gravitational factor<br>17.7 Termination criteria<br>17.8 User-defined parameters of the GSA<br>17.9 Pseudo code of the GSA&nbsp;&nbsp;&nbsp; <br>17.10 Conclusion<br>17.11 References<br><br>Chapter 18: The Bat Algorithm (BA)<br>Summary<br>This chapter describes the bat algorithm (BA) that is a relatively recent meta-heuristic optimization algorithms. The chapter starts with a brief literature review of the BA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>18.1 Introduction<br>18.2 Mapping behavior of microbats into bat algorithm (BA)<br>18.3 Creating the initial population<br>18.4 Movement of virtual bats<br>18.5 Local search and random fly<br>18.6 Loudness and pulse emission<br>18.7 Termination criteria^8.8 User-defined parameters of the BA<br>18.9 Pseudo code of the BA<br>18.10 Conclusion<br>18.11 References<br><br>Chapter 19: The Plant Propagation Algorithm (PPA)<br>Summary<br>This chapter describes the plant propagation algorithm (PPA) which simulates the multiplication of some plants such as the strawberry plant. The chapter starts with a brief literature review of the PPA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>19.1 Introduction<br>19.2 Mapping the natural process into planet propagation algorithm (PPA)<br>19.3 Creating the initial population of plants<br>19.4 Normalizing the fitness function<br>19.5 Propagation<br>19.6 Elimination of extra solutions<br>19.7 Termination Criteria<br>19.8 User-defined parameters of the PPA<br>19.9 Pseudo code of the PPA<br>19.10 Conclusion<br>19.11 References<br><br>Chapter 20: The Water Cycle Algorithm (WCA)<br>Summary<br>This chapter describes the water cycle algorithm (WCA) that is a relatively recent meta-heuristic optimization algorithm. The chapter starts with a brief literature review of the WCA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>20.1 Introduction<br>20.2 Mapping the water cycle into the water cycle algorithm (WCA)<br>20.3 Creating the initial population<br>20.4 Classified raindrops<br>20.5 Flowing streams to the rivers or sea<br>20.6 Evaporation condition20.7 Raining process<br>20.8 Termination criteria<br>20.9 User-defined parameters of the WCA<br>20.10 Pseudo Code of the WCA<br>20.11 Conclusion<br>20.12 References<br><br>Chapter 21: Symbiotic Organisms Search (SOS) algorithm<br>Summary<br>This chapter explains the symbiotic organisms search (SOS) algorithm, a recently-developed meta-heuristic algorithm which is inspired by symbiotic relationships among species. The chapter starts with a brief literature review of the SOS algorithm’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>21.1 Introduction<br>21.2 Mapping symbiotic relationships into symbiotic organisms search (SOS)<br>21.3 Creating the initial ecosystem<br>21.4 Mutualism<br>21.5 Commensalism<br>21.6 Parasitism<br>21.7 Termination criteria<br>21.8 Pseudo code of the SOS<br>21.9 Conclusion<br>21.10 References<br><br>Chapter 22: The Comprehensive evolutionary algorithm (CEA)<br>Summary<br>This chapter explains a new meta-heuristic optimization algorithm called comprehensive evolutionary algorithm (CEA). This algorithm combines and takes advantages of some aspects of different algorithms, especially the genetic algorithm (GA) and the honey bee mating optimization (HBMO) algorithm. The chapter starts with a brief literature review of the CEA’s development, important modifications that it has experienced, and its applications in engineering domains. The basic idea underlying the algorithm is presented and mapped to its mathematical statement. Afterwards, the steps of the algorithm are described in detail. A pseudo code of the algorithm is presented to serve as an easy and sufficient guideline for its coding.<br>22.1 Introduction22.2 Foundation of the CEA<br>22.3 Generating the initial population<br>22.4 Selection<br>22.5 Reproduction<br>22.7 Input information of the CEA<br>22.8 Termination criteria<br>22.9 Pseudo code of the CEA&nbsp;&nbsp;&nbsp; <br>22.10 Conclusion<br>22.11 References<br><br>